Introducing the California Nitrogen Assessment Appendix 1 ... · CALIFORNIA NITROGEN ASSESSMENT...

CHAPTER ONE

Introducing the California Nitrogen Assessment

Appendix 1.1 Who was involved in the California Nitrogen Assessment?

Lead Authors: K. THOMAS, D. LIPTZIN, AND T.P. TOMICH

Contributing Authors: M. COLEY, R. DAHLGREN, B. HOULTON, K. SCOW, A. WHITE

This is an appendix to Chapter 1 of The California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. Additional information about the California Nitrogen

Assessment (CNA) and appendices for other chapters are available at the Agricultural Sustainability Institute website: asi.ucdavis.edu/nitrogen

Suggested citation:

K Thomas, D Liptzin, TP Tomich, M Coley, R Dahlgren, B Houlton, K Scow, and A White. “Appendix 1.1: Who was involved in the California Nitrogen Assessment?” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.

CALIFORNIA NITROGEN ASSESSMENT

APPENDIX 1.1 WHO WAS INVOLVED IN THE CALIFORNIA NITROGEN ASSESSMENT? 2

Principal Investigators Sonja Brodt, Academic Coordinator,

Agricultural Sustainability Institute, UC Davis Randy Dahlgren, Director emeritus, Kearney

Foundation of Soil Science, UC Davis Kate Scow, Director, Russell Ranch Sustainable

Agriculture Facility, UC Davis Thomas P. Tomich, Director, Agricultural

Sustainability Institute, UC Davis Technical Advisory Committee Randy Dahlgren, Director emeritus, UC Kearney

Foundation of Soil Science Thomas Harter, ANR CE Specialist, UC Davis Ermias Kebreab, Professor, UC Davis Frank Mitloehner, Associate Professor, UC

Davis Dan Putnam, ANR CE Specialist, UC Davis Kate Scow, Deputy Director, Agricultural

Sustainability Institute, UC Davis Johan Six, Associate Professor, UC Davis Daniel Sumner, Director, UC Agricultural Issues

Center, UC Thomas P. Tomich, Director, Agricultural

Sustainability Institute, UC Davis Assessment Personnel Colin Bishop, Communications and Outreach

Fellow Mariah Coley, Program Representative Antoine Champetier, Policy/Economics Fellow Karen Curley, Administrative Assistant Van Ryan Haden, Agriculture and Ecosystem

Services Fellow Dan Liptzin, Biogeochemistry Fellow Stephanie Ogburn, Communications and

Outreach Fellow Todd Rosenstock, Best Practices and Technical

Options Fellow Karen Thomas, Senior Writer Aubrey White, Communications and Outreach

Fellow

Stakeholder Advisory Committee Pelayo Alvarez, California Rangeland Conservation Coalition Program

Director (former), Defenders of Wildlife Ted Batkin, President (former), Citrus Research Board Steve Beckley, Executive Director, Organic Fertilizer Association of California Don Bransford, Chairman (former), CA Rice Producer’s Group, California

Rice Commission and President, Bransford Farms; member of CA State Board of Food and Agriculture

Renata Brillinger, Executive Director, California Climate and Agriculture Network

Cynthia Cory, Director, Environmental Affairs, California Farm Bureau Federation

Bob Curtis, Associate Director of Agricultural Affairs, Almond Board of California

Michael Dimock, President, Roots of Change Laurel Firestone, Co-Executive Director, Community Water Center Hank Giclas, Sr. Vice President, Strategic Planning, Science and Technology,

Western Growers Association Joseph Grant, Farm Advisor, University of California Cooperative Extension,

San Joaquin County Woody Loftis, EPA Liaison, United States Department of Agriculture-Natural

Resources Conservation Service Tim Johnson, President-CEO, California Rice Commission Matthew Keeling, California Regional Water Quality Control Board, Central

Coast Region David Lighthall, Health Science Advisor, San Joaquin Valley Air Pollution

Control District Karl Longley, Coordinator of Water Resources Programs, California Water

Institute Jim Lugg, Consultant, Fresh Express/Chiquita Paul Martin, Director of Environmental Services (former), Western United

Dairymen) Albert Medvitz, McCormack Sheep and Grain Rob Mikkelsen, Western North America Director, International Plant

Nutrition Institute Belinda Morris, Climate and Land Use Program Officer, David and Lucille

Packard Foundation Alberto Ortiz, General Manager, Ag Services (Salinas) Renee Pinel, President/CEO, Western Plant Health Association Brise Tencer, Executive Director, Organic Farming Research Foundation Bruce Rominger, Owner, Rominger Brothers Farms David Runsten, Policy Director, Community Alliance with Family Farmers Ann Thrupp, Executive Director, Berkeley Food Institute at UC Berkeley Kathy Viatella, California Water Foundation Program Manager, Resources

Legacy Fund Former members: Allen Dusault, Program Director, Sustainable Conservation Ian Greene, Research Programs Manager, California Strawberry Commission Larry Glashoff, Horticultural Tech Manager, Hines Nursery Claudia Reid, California Certified Organic Farmers Edward Hard, CDFA Fertilizer Research and Education Program (former) Don Hodge, Environmental Protection Specialist, US Environmental

Protection Agency

CHAPTER ONE


Appendix 1.2 Organizations contacted as part of the California Nitrogen Assessment





Suggested citation:

K Thomas, D Liptzin, TP Tomich, M Coley, R Dahlgren, B Houlton, K Scow, and A White. “Organizations contacted as part of the California Nitrogen Assessment.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 1.2 ORGANIZATIONS CONTACTED AS PART OF THE CNA 2

1.2 Organizations Contacted as Part of the California Nitrogen Assessment

Includes organizations contacted to participate in consultations and to request specific information as part of the research process. While not all participated in the assessment or provided feedback, best efforts were made to be inclusive and obtain a diversity of perspectives. (Listed alphabetically by group)

Government Agencies & Organizations: California Association of Sanitation Agencies California Department of Food and Agriculture Fertilizer

Research and Education Program (FREP) California Department of Food and Agriculture Marketing

Branch California Department of Pesticide Regulation California Department of Water Resources California Environmental Protection Agency California Environmental Protection Agency State Water

Resources Control Board California Integrated Waste Management California Water Resources Control Board Monterey Bay National Marine Sanctuary Monterey County Water Resources Agency National Aeronautics and Space Administration (NASA) San Joaquin Valley Air Pollution Control District

United States Department of Agriculture (USDA) United States Environmental Protection Agency Region 9

United States Geological Survey (USGS) California Water Science Center

Health Organizations: Clean Water Action Community Water Center Texas A&M University School of Rural Public Health The Pacific Institute Environmental Organizations: Californians Against Waste Defenders of Wildlife Environmental Defense Fund Environmental Working Group Food and Water Watch NewFields Roots of Change Sustainable Conservation

Farms, Ranches, Nurseries and Vineyards: Christensen & Giannini D'Arrigo Brothers Co. Earthbound Farm Ferguson Farming Company Fetzer/Bonterra Vineyards Grimmway Farms Harris Farm Hines Nursery John Pryor Co. McCormack Sheep and Grain Ocean Mist Farms Red Rock Ranch Rio Farms Rominger Brothers Farms Woolf Enterprises Farm-Related Organizations: American Farmland Trust California Certified Organic Farmers California Farm Bureau Federation Central Coast Water Quality Preservation Community Alliance with Family Farmers Grower-Shipper Association Royal Packing Co. SureHarvest Western Growers Association

Fertilizer Industry Groups: International Plant Nutrition Institute Organic Fertilizer Association of California Western Plant Health Association Other Non-Profit Organizations: Central Valley Salinity Coalition


APPENDIX 1.2 ORGANIZATIONS CONTACTED AS PART OF THE CNA 3

Commodity groups: Almond Board of California California Alfalfa and Forage Association California Apple Commission California Association of Winegrape Growers California Cattlemen’s Association California Celery Research Advisory Board California Citrus Mutual California Citrus Nursery Program California Cling Peach Association California Cut Flower Commission California Dairy Research Foundation California Dried Plum Board California Dry Bean Advisory Board California Fresh Carrot Advisory Board California Garlic and Onion Research Advisory Board California Grape Rootstock Improvement Commission California Grape & Tree Fruit League California Leafy Greens Research Board California Melon Research Board California Pepper Commission California Pistachio Board California Pistachio Research Board California Potato Research Advisory Board California Poultry Federation California Raisin Marketing Board California Rice Commission California Rice Research Advisory Board California Specialty Crops Council California Strawberry Commission California Sustainable Winegrowing Alliance California Table Grape Commission California Tomato Farmers California Tomato Growers Association California Tree Fruit Agreement California Walnut Commission California Wild Rice Advisory Board California Winegrape Inspection Marketing Program Citrus Research Board Golf Course Superintendents Association of America Lodi Winegrape Commission Napa Grape Growers Association Northern California Turf and Landscape Council Nursery Growers Association Processing Strawberry Advisory Board Processing Tomato Advisory Board

Commodity groups (continued): Southern California Turfgrass Council Western United Dairymen Wine Institute Research Organizations: California Water Institute The John Muir Institute at UC Davis The Kearney Foundation of Soil Science The University of California Agricultural Issues Center Washington State University, Vancouver

CHAPTER ONE


Appendix 1.3 List of questions and issues raised by stakeholders





Suggested citation:

K Thomas, D Liptzin, TP Tomich, M Coley, R Dahlgren, B Houlton, K Scow, and A White. “List of questions and issues raised by stakeholders.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 1.3 LIST OF QUESTIONS AND ISSUES RAISED BY STAKEHOLDERS 2

1.3 List of questions and issues raised by stakeholders

Questions are grouped by topic, and have not been edited.

Science and research questions

Nitrogen & climate intersects

What is the climate change impact of agricultural N application? What does existing science say about how fertilization and irrigation interact to produce nitrous oxide? What are the mechanisms by which N application leads to GHG emissions? What does the science tell and not tell us about the interactions of fertilizer and water in producing

nitrous oxide? What about N trading? N credits? In uncertain climate conditions (change), what impact would this have on N needs for different

crops/regions/etc.? Can composting processes lead to greenhouse gas emissions? What measures to prevent N emissions? Organic practices How does organic management impact how N moves through agroecosystems? Does using compost as primary fertilizer input lead to reduced N leakages? How do organic/sustainable systems manage nitrogen differently? With what differences in quality and

quantity? What rules to follow: organic N, commercial N, livestock N Organic N use: research, utilization, mineralization rate Compost as benefit to crop What are the food safety implications with use of compost? What potential roles can compost play in: (1) Reducing need for synthetic N, (2) Reducing N offgassing

during application, (3) reducing N runoff during/after fertilization, (4) stabilizing manure? Flows How does nitrogen flow through California? Can this be graphically represented? What parts of Calif. have too much N and what form does this N take? Can N flows and losses be quantified? What are the major sources of N in California? Can the N from these sources be tracked/tagged? From a systems perspective, what parts of N fate can be controlled? Are there ways to “tag” nitrate/nitrogen so one can identify what source it is coming from? What are the implications for nitrogen flows based on the future scenarios of climate change and water

availability? Where in the state are the major impacts of too much N? Groundwater, coastal blooms, etc? Is there a

map? Quantification of nitrogen flows and losses: emissions from N application, groundwater migration,

consumption, other.



Need total systems perspective of N fate – what can be controlled or not NO3 sources determination What are the geographic areas with the greatest concentrations of N in the environment? What is the role of rangelands in N cycling? Who are the generators (sources) of N? How will we identify if nitrogen overuse occurs? Nitrogen and water Can water use/conservation practices reduce N leakages? How can a farmer minimize movement of NO3 to surface or groundwater? What quantity of N in groundwater is “legacy” and how can nitrates in groundwater be mitigated? What is the timing between N application and impact on groundwater? Legacy N: cause and effect, time between use and impact. Impact and response to legacy N pollution, mitigation strategies What are the synergies between conservation of N and conservation of water? What specific steps should a farmer take to minimize movement of NO3 to surface water or ground

water? Is appropriate guidance available for each of these steps? Non-agricultural What are the non-agricultural sources of nitrogen such as vehicles and other combustion and what are

their relative impacts? What are non-ag sources of nitrogen and what is their contribution to nitrogen flows in California? What is the N impact of motor vehicles? How does reactive N generated by vehicles and other combustion fit into the total assessment? What is the contribution to N pollution of Ag compared to other sources? Other Are all nitrogen sources evaluated the same? Feasibility of achieving endpoints: source-specific, long vs. short term, varied level of investment What is the effect of N pollution on wildlife/threatened and endangered species? What is realistic in improving efficiency, environmental performance? What’s doable? Cost of N management: by source, by method, globally to achieve and points Efficiency vs. capture and reuse Crop quality: N impact on What is the role of N in anaerobic systems? What are the relative contributors to N loading of Livestock operations compared to crops operations?

Crop to crop? Recommendations

Is it desirable for assessment to use DWR Bulletin 160-2009 scenarios? Don’t reinvent the wheel on N use Need to recognize existing research and extension work



Need to consult with UC Davis researchers who study N Will the assessment include a white paper on the environmental impact of nitrogen pollution? Practices and management questions Current knowledge What are current N application recommendations based on? What are the most effective ways for dealing with excess nitrogen? What are current practices that hold promise for N control? What is the best way to measure N efficiency? What is the best way to measure N needs? Do N recommendations change when shifting from maximizing yield to minimizing N -use? What are the most effective solutions demonstrated to date for dealing with excess nitrogen? What farming and ranching practices hold promise for nitrogen control? Are UC Davis N recommendations based on current production conditions? What is the current state of practice in terms of BMPs with nitrogen/nutrient management? Percent of

users quantified (qualified???) in California? How could policy further data collection? Do the potential "savings" from limiting N applications outweigh the risk of reduced yields? Plant health How does amount, form, and application timing of N impact crop yields quality, disease and pest

resistance? How does it interact with plant varieties? Do we know enough about how: (1) amount of N,( 2) form of N applied, (3) how it is applied, and (4)

When it is applied; affects crop yields and crop quality (e.g., keeping quality, flavor components, negative chemical profiles)

Disease and pest vulnerability with improper N application What role does variety selection and plant improvement play in efficiently using N (either by reducing N

needed or by uptaking excess N) ? Risk management in N use – insurance What other issues could arise from inadequate or excessive N availability? Ex. Excessive could increase

foliar development - increased habitat for pests? Efficiency/best management practices What are the most efficient ways to apply N depending on plant variety and crop type? Can managing water differently lead to N efficiencies? What are BMPs for reducing air and water emissions from N? How can producers of excess N link with producers who need N? Which crop sectors/systems are the most inefficient in terms of N fertilizer application? Can they be made more efficient? Can you provide estimates regarding how much overfertilization? Most efficient N use application: how do you manage field and plant variability? Is more outreach and demo needed to convince ag industry that the benefits of fertigation outweigh the

increased cost of liquid forms of N?



Process-based N model needs to be turned into a management tool Nitrogen and water management What are BMPs that both address reduced and direct N2O emissions and indirect N2O Emissions via water? E.g., reduce leaching through LAND management practices What is optimum N application for crop health and to minimize leaching to groundwater? 0.9 - 1.4 x crop

uptake?! How can producers of excess N (dairies for example) link w/ producers that could use N as a soil

amendment? Can we find ways to reduce overall use of synthetic fertilizers? What are the ag practices most suited to conserving N? Least suited? Other What gaps exist in research related to impacts on yield with respect to excess or insufficient N

application? How will current practices be assessed? What is source of info – who does farmer trust? Cover crops – work both ways – pull N out and put into soil What is the role of other technological work to address N impacts? Are there solutions to the problems raised (made apparent) by looking at N? What is the best way to measure N efficiency rate or use to forecast needs? How to measure changes in cultural practices and past/current nitrogen demands? Do University recommendations reflect the N demands of current crop production? Policy and economics questions Economic incentives Could markets be created that allow farmers to make money for reducing N impact? N impact on global warming: opportunity to $ How are we going to create fiscal and other incentives for the adoption of reactive nitrogen control? What kinds of incentives, if any are needed, would speed adoption of better N management? Is Ag getting the credit for current use of N? What about N trading? N credits? Regulatory mechanisms How can regulations be streamlined so they do not offer competing policies and so that reactive N is not

simply displaced into other forms? What are the most effective policy instruments to motivate farmers to implement BMPs for nitrogen

management? How to integrate competing regs! How are different regulatory bodies coordinating or not to address nitrogen issues? How might policy be used more effectively to both monitor and address non-point source N pollution

from the ag sector?



Are there policy options for reducing auto-based N emissions? How will the work of this project tie into the implementation of AB 32 and SB 375? How much are policy makers/regulators coordinated on developing a cohesive N policy that will be

workable for N users? What are the most effective policy instruments to motivate adoption of BMPs for nitrogen mgmt? What are the cities’ roles in solving the problem of N? Endpoints for nitrogen management – air, water, economic impacts Cost analysis What are the costs and benefits of Nitrogen Use Efficiency Practices? What is the cost of the health impacts of excess N in environment? What are the costs of N management (by source and by method)? Are there cost-effective treatment options for groundwater nitrate contamination? What is the feasibility of wellhead protection and regional treatment facilities for communities impacted

by high-nitrate levels in drinking water? Do solutions to N problems make economic sense? What are cost/benefits of NUE practices? Best way to implement these? How might N management tools and documentation be structured so they’re appropriate for a variety of

production models/systems and farm sizes? Cost of N management: by source, by method, globally to achieve and points How can health impacts be integrated into evaluation of nitrate mitigation alternatives? Cost of N management: by source, by method, globally to achieve and points Other Can NUE practices be scaled across a wide range of production models/systems and farm sizes? What would happen if we could no longer rely on fossil fuels as N source? Solutions need support/technical assistance Role of the “social” institutions? Social justice implications – emissions, jobs, etc. Human Health What is the state of understanding on the link between nitrates and blue baby syndrome, kidney, spleen

and bladder cancers, Alzheimer’s, diabetes and Parkinson’s. What are the food safety implications of elevated levels of N in crops? What are safe levels of N in crops (tissue) for humans? Can some crops “super-accumulate” N to reach levels that are injurious to consumers? Communications and outreach How do we communicate complexity of N system and problems to public What is the understanding among producers & policymakers of N impacts? And what’s the gap between

myth and reality? Are there maps showing impacts of N?



Who are the stakeholders? Are there ongoing outreach efforts to positively incentivize farmers to document their N use? Can tools be developed for farmers to assess N-use efficiency? What outreach is being done to farmers regarding N and greenhouse gas issues?

CHAPTER ONE


Appendix 1.4 Review Editors for the California Nitrogen Assessment





Suggested citation:

K Thomas, D Liptzin, TP Tomich, M Coley, R Dahlgren, B Houlton, K Scow, and A White. “Review editors for the California Nitrogen Assessment.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 1.4 REVIEW EDITORS FOR THE CALIFORNIA NITROGEN ASSESSMENT 2

1.4 Review Editors for the California Nitrogen Assessment

Chapter Review Editors provided feedback for a specific chapter(s) while the Review Editor provided oversight for the whole assessment. Review Editor Alan Townsend, Duke University Chapter Review Editors

Chapter 1: Introducing the California Nitrogen Assessment Neville Ash, United Nations Environment Program

Chapter 2: Underlying Drivers of Nitrogen Flows in California Eric Lambin, Stanford University

Chapter 3: Direct Drivers of California’s Nitrogen Cycle Eric Lambin, Stanford University

Chapter 4: A California Nitrogen Mass Balance for 2005 Jim Galloway, University of Virginia

Chapter 5: Ecosystem Services and Human Well-Being Peter Vitousek, Stanford University Steve Polasky, University of Minnesota Paul English, California Department of Public Health

Chapter 6: Scenarios for the Future of Nitrogen Management in California Monika Zurek, Climate Focus

Chapter 7: Responses: Technologies and Practices Cliff Snyder, International Plant Nutrition Institute Chapter 8: Responses: Policies and Institutions

David Zilberman, University of California Berkeley

CHAPTER TWO

Underlying Drivers of Nitrogen Flows in California

Appendix 2.1 Income and Patterns of Demand for Food

Lead Authors:

A. CHAMPETIER, D. SUMNER, AND T.P. TOMICH

Contributing Authors:

S. BRODT, M. COLEY, V.R. HADEN, M. KREITH, J.T. ROSEN-MOLINA, AND K. THOMAS



Suggested citation:

A Champetier , D Sumner, TP Tomich, S Brodt, M Coley, VR Haden, M Kreith, JT Rosen-Molina, and K Thomas. “Appendix 2.1: Income and patterns of demand for food.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 2.1 INCOME AND PATTERNS OF DEMAND FOR FOOD 1

2.1 Income and Patterns of Demand for Food

Elasticity estimates which take into account variations in food prices provide an indicator of the relationship between income per capita and food demand. Alderman (1986) compared 15 studies, which covered 11 countries, and concluded that while all consumers readily change consumption patterns when prices for food items increase, the poor are more likely to make such substitutions than the well-off. Such substitutions by the poor are in addition to changes that they make that are attributable to a reduction in real income when food prices increase.

For the United States, it is provisionally agreed by most that consumers’ responses to changes in income, approximated by changes in food expenditures, vary by commodity and are high for foods that have high price elasticities (e.g., fruits, vegetables, and juice). Responses are low for foods that have low price elasticities (e.g., eggs), reflecting that consumers do not significantly change their consumption when the prices for these commodities change (Huang and Lin, 2000; Okrent and Alston, 2011).

The findings for low income countries are more speculative and subject to methodological debates over data aggregation and the timing of behavior changes. Alderman (1986) estimated that families that consume 1,750-2,000 calories per person per day will increase their food expenditure by 8.2% for an income increase of 10% - an income elasticity of 0.82. However, calorie intake will only increase by 4.8% as some of the increase in expenditure is used to increase perceived diet quality. In contrast, Dawson and Tiffin (1998) estimate an income elasticity of calorie intake of 0.34 for the period 1961-1992 in India.


APPENDIX 2.1 INCOME AND PATTERNS OF DEMAND FOR FOOD 2

References Alderman, H., 1986. The effect of food price and income changes on the acquisition of food by low-

income households. International Food Policy Research Institute, Washington, D.C. Dawson, P.J., Tiffin, R., 1998. Estimating the Demand for Calories in India. American Journal of

Agricultural Economics 80, 474–481. doi:10.2307/1244550 Huang, K.S., Lin, B.H., 2000. Estimation of Food Demand and Nutrient Elasticities from Household

Survey Data (Technical Bulletin No. TB-1887). USDA ERS (Economic Research Service). Okrent, A.M., Alston, J.M., 2011. Demand for Food in the United States: A Review of Literature,

Evaluation of Previous Estimates, and Presentation of New Estimates of Demand (Monograph No. 48). Giannini Foundation of Agricultural Economics, University of California.

CHAPTER THREE

Direct Drivers of California’s Nitrogen Cycle

Appendix 3.1 Average N Fertilizer Application Rates By Crop, 1973 and 2005

Lead Authors:

T.S. ROSENSTOCK AND T.P. TOMICH


H. LEVERENZ, D. LIPTZIN, D. MEYER, D. MUNK, P.L. PHELAN, AND J. SIX

This is an appendix to Chapter 3 of The California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. Additional information about the California

Nitrogen Assessment (CNA) and appendices for other chapters are available at the Agricultural Sustainability Institute website: asi.ucdavis.edu/nitrogen

Suggested citation:

TS Rosenstock, TP Tomich, H Leverenz, D Liptzin, D Meyer, D Munk, PL Phelan, and J Six. “Appendix 3.1: Average N Fertilizer Application Rates By Crop, 1973 and 2005.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.1 AVERAGE N FERTILIZER APPLICATION RATES BY CROP, 1973 AND 2005 2

Appendix 3.1 Average N fertilizer application rates by crop, 1973 and 2005

Area is based on a five-year average centered on 1973 and 2005. The average N application rate has only increased 25% over 33 years. However, the magnitude and direction of change is crop specific. Four of the thirty-three commodities use more than 50% of total N use accounted for in this analysis: almond, cotton, rice, and wheat. Source: Rosenstock et al., 2013. Area (ha) N rate (kg / ha)

Δ N rate (%) N (% total)

Crop 1973 2005 1973 2005 1973 2005 Almond 86462 236800 142 201 41 6 15 Avocado 8144 24728 140 125 -11 1 1 Beans, dry 67760 25600 57 102 79 2 1 Broccoli 17432 47000 204 213 4 2 3 Carrots 12592 28248 134 242 80 1 2 Cauliflower 9264 13624 205 267 30 1 1 Celery 7220 10296 321 290 -10 1 1 Corn, sweet 5680 10224 162 239 47 0 1 Cotton 372840 250400 122 195 60 24 16 Grapes, raisin 96080 96000 64 49 -23 3 2 Grapes, table 26432 33280 64 49 -24 1 1 Grapes, wine 65992 191120 59 30 -49 2 2 Lemons 16608 19360 186 138 -26 2 1 Lettuce 58048 92960 178 216 21 5 6 Melons, cantaloupe 19016 17840 106 182 71 1 1 Melons, watermelon 4480 4768 178 169 -5 0 0 Nectarines 4184 13480 147 116 -21 0 1 Onions 11400 18744 164 237 45 1 1 Oranges 74416 76960 73 106 46 3 3 Peaches, clingstone 20200 11752 149 114 -23 2 0 Peaches, freestone 8440 13360 149 127 -15 1 1 Peppers, bell 3520 8280 181 388 114 0 1 Peppers, chile 1887 2184 181 336 85 0 0 Pistachio 41040 166 178 7 2 Plums, dried 33120 27040 106 146 37 2 1 Plums, fresh 9416 12880 123 116 -6 1 0 Potato 28024 16328 212 278 31 3 1 Rice 165200 214320 96 146 52 8 10 Strawberry 3448 13472 178 216 21 0 1 Tomatoes, fresh market 11272 15520 159 198 24 1 1 Tomatoes, processing 88776 111760 159 204 28 7 7



Walnut 63616 86080 134 154 15 4 4 Wheat 270240 157920 99 198 101 14 10 Average 145 181 25 Reference Rosenstock, T.S., Liptzin, D., Six, J., Tomich, T.P., 2013. Nitrogen fertilizer use in California:

Assessing the data, trends and a way forward. California Agriculture 67, 68–79. doi:10.3733/ca.E.v067n01p68

CHAPTER THREE


Appendix 3.2 Are University N Rate Guidelines Current?

Lead Authors:






Suggested citation:

TS Rosenstock, TP Tomich, H Leverenz, D Liptzin, D Meyer, D Munk, PL Phelan, and J Six. “Appendix 3.2: Are University N Rate Guidelines Current?” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.2 ARE UNIVERSITY N RATE GUIDELINES CURRENT? 2

Appendix 3.2 Are University N Rate Guidelines Current?

Since World War II and continuing to the present day, University of California (UC) research has established crop-specific “N rate guidelines” (Proebsting, 1948; Hartz and Bottoms, 2009). An N rate guideline is a range of N application rates expressed as a unit of weight area-1 (e.g., kg ha-1) that are generally able to achieve maximum yield. Most often, N application rate guidelines are printed in University of California Department of Agriculture and Natural Resources (UC DANR) publications and are extended to producers through information channels including: bulletins, production manuals, and field days. The California Nitrogen Assessment analyzed the current status of N rate guidelines for 33 major commodities grown in California and found publications from UC DANR with N guidelines published within the last 25 years for 28 of the 33 crops (Appendix 3.3). Guidelines for 58, 64, and 86% of the 28 commodities had been published within the last 5, 10, and 15 years, respectively. In most cases, more recent publications were revisions of previous guidelines to incorporate new research, changes in management practices, and crop genetics.

Current N guidelines vary widely between their lowest and highest values (Table 3.2.1.). The minimum suggested application rate is often almost 100% less than the maximum rate for any single commodity. The large range can be justified by the diversity of cropping systems, technologies, and growing conditions for any one commodity. The combination of which can create large differentials in crop demand, system efficiency, and the amount of fertilizer demand. When comparing current estimated N application rates with the guidelines, the estimated current rates were above the guidelines for 45% of the 33 crops, and within the guidelines for 55% of the crops. For those estimates that were within the guidelines, 31% were in the top quartile of the guideline. These findings suggest either the guidelines underestimate the N required or average producers over-apply N fertilizer for a considerable number of crops in California.


APPENDIX 3.2 ARE UNIVERSITY N RATE GUIDELINES CURRENT? 3

Table 3.2.1. Comparison of average 2005 fertilizer N application rates to University guidelines. The comparison provides a measure to determine if average N application rates are within that suggested by research results. Application rates that exceed the maximum in the guidelines suggest that either the guidelines do not reflect cropping conditions or growers over-apply N.

Crop type N Range of guideline (% ± SD)

Within1 (%)

Over2 (%)

Mean surplus3 (lbs. N per acre± SD)

Field crops 4 73 ± 46 100 - - Perennials 12 88 ± 54 50 33 14 ± 12 Vegetables and annual fruits 12 101 ± 83 58 42 53 ± 47 All crops 28 90 ± 65 57 36 36 ± 39

1The percentage of crops with an average N application rate within the UC guidelines. 2The percentage of crops with an average N application rate exceeding the maximum listed in the UC guidelines. 3The amount of N applied above the maximum rate in the guidelines.

References

Hartz, T.K., Bottoms, T.G., 2009. Nitrogen Requirements of Drip-irrigated Processing Tomatoes. HortScience 44, 1988–1993.

Proebsting, E., 1948. Nitrogen fertilizer: Usually beneficial to soils of California. California Agriculture 2, 10–10.

CHAPTER THREE


Appendix 3.3 Published University of California N Fertilizer Rate Guidelines for Select Crops

Lead Authors:






Suggested citation:

TS Rosenstock, TP Tomich, H Leverenz, D Liptzin, D Meyer, D Munk, PL Phelan, and J Six. “Appendix 3.3: Published University of California N Fertilizer Rate Guidelines for Select Crops.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.3 PUBLISHED UNIVERSITY OF CALIFORNIA N FERTILIZER RATE GUIDELINES 2

Appendix 3.3 Published University of California N Fertilizer Rate Guidelines for Select Crops

N guidelines (lbs. per acre)

Crop Minimum Maximum Source Alfalfa 0 50 Summers and Putnam 2008 (Pub #3512) Almond 100 200 Micke 1996 (Pub #3364) Avocado 67 100 Faber et al. 2011 (Pub #3436), Lovatt 2001,

Wolstenholme 2004 Bean, dry 86 116 Long et al. 2010 (Pub #8402) Broccoli 100 200 LeStrange et al. 2010 (Pub # 7211) Carrot 100 250 Nuñez et al. 2008 (Pub #7226) Celery 200 275 Daugovish et al. 2008 (Pub #7220) Corn 150 275 Corn, sweet 100 200 Smith et al. 1997 (Pub #7223) Cotton 100 200 Hake et al. 1996 (Pub #3352) Environment horticulture

Newman 2009 (Pub #3508)

Grape, raisin 20 60 Christensen 2000 (Pub #3393) Lawn (Heavy soil)

174 261 Harivandi and Gibeault 1997 (Pub #7227)

Lawn (Shade)

87 130 Harivandi and Gibeault 1996 (Pub #7214)

Lemon Ingels et al. 1994 (Pub #21521) Lettuce 170 220 Turini 2011 (Pub #7215), Smith et al. 2011b

(Pub #7216) Melon, cantaloupe

80 150 Hartz et al. (2008b) (Pub #7218)

Melon, watermelon

160 Baameur et al. 2009 (Pub #7213)

Melons (mixed) 100 150 Mayberry et al. 1996 (Pub #7209) Nectarine 100 150 Strand 1999 (Pub #3389) Oats 50 120 Munier et al. 2006 (Pub #8167) Onion 100 400 Smith et al. 2011a (Pub #7242) Oranges Ingels et al. 1994 (Pub #21521) Peach, cling 50 100 Norton et al. 2007a (Pub #8276) Peach, free 50 100 Norton et al. 2009 (Pub #8358) Pepper, bell 180 240 Hartz et al. 2008a (Pub #7217)



Pepper, chile 150 200 Smith et al. 2011c (Pub #7244) Pistachios 100 225 Beede et al. 2005 Plums, dried (prunes)

100 Norton et al. 2007b (Pub #8264)

Plums, fresh 110 150 La Rue and Johnson 1989 (Pub #3331) Potato Strand et al. 2006 Pub. #3316. Rice 110 145 Mutters and Thompson 2009 (Pub #3514) Safflower 100 150 Kaffka and Kearney 1999 (Pub #21565) Strawberry 150 300 Strand et al. 2008 (Pub #3351) Tomatoes, fresh market

125 350 Le Strange et al. 2000 (Pub #8017)

Tomatoes, processing

100 150 Hartz et al. 2008 (Pub #7228)

Walnuts 150 200 Anderson 2006 (Pub #21623), Ramos 1997 (Pub. #3373)

Wheat 100 240 Munier et al. 2006 (Pub #8167)

References

Anderson, K., 2006. Guide to Efficient Nitrogen Fertilizer Use in Walnut Orchards (Publication #21623). University of California Agriculture and Natural Resources.

Baameur, A., Hartz, T.K., Turini, T., Natwick, E., Takele, E., Aguiar, J., Cantwell, M., Mickler, J., 2009. Watermelon Production in California.

Beede, R., Brown, P.H., Kallsen, C., Weinbaum, S.A., 2005. Diagnosing and Correcting Nutrient Deficiencies, in: Ferguson, L. (Ed.), Pistachio Production Manual. University of California, Davis, p. Chapter 16.

Christensen, L.P., 2000. Raisin Production Manual (Publication #3393). University of California Agriculture and Natural Resources.

Daugovish, O., Smith, R., Cahn, M.D., Koike, S.T., Smith, H., Aguiar, J., Quiros, C., Cantwell, M., Takele, E., 2008. Celery Production in California (No. 7220). University of California Division of Agriculture and Natural Resources.

Faber, B.A., Wilen, C.A., Morse, J.G., Eskalen, A., 2011. Avocado: UC IPM Pest Management Guidelines - Publication 3436 (Rev. 2015) (No. 3436).

Hake, S.J., Kerby, T.A., Hake, K.D., 1996. Cotton Production Manual. University of California Agriculture and Natural Resources.

Harivandi, A., Gibeault, V., 1997. Managing Lawns on Heavy Soils. Harivandi, A., Gibeault, V., 1996. Managing Lawns in Shade.



Hartz, T.K., Cantwell, M., LeStrange, M., Smith, R., Aguiar, J., Daugovish, O., 2008a. Bell Pepper Production in California.

Hartz, T.K., Cantwell, M., Mickler, J., Mueller, S., Stoddard, S., Turini, T., 2008b. Cantaloupe Production in California (Publication 7218).

Hartz, T.K., Miyao, G., Mickler, J., Le Strange, M., Stoddard, S., Nuñez, J., Aegerter, B., 2008. Processing Tomato Production in California.

Ingels, C., 1994. Protecting Groundwater Quality in Citrus Production - Publication 21521. Kaffka, S.R., Kearney, T.E., 1999. Safflower Production in California. University of California

Agriculture and Natural Resources. La Rue, J.H., Johnson, R.S., 1989. Peaches, Plums, and Nectarines: Growing and Handling for

Fresh Market (Publication 3331). University of California Agriculture and Natural Resources.

LeStrange, M., Cahn, M.D., Koike, S.T., Smith, R.F., Daugovish, O., Fennimore, S.A., Natwick, E., Dara, S.K., Takele, E., Cantwell, M., 2010. Broccoli Production in California, Publication 7211. U.C. Sustainable Agriculture and Education Program, Division of Agriculture and Natural Resources.

LeStrange, M., Schrader, W.L., Hartz, T.K., 2000. Fresh-Market Tomato Production in California. University of California Division of Agriculture and Natural Resources.

Long, R., Temple, S.R., Schmierer, J., Canevari, M., Meyer, R.D., 2010. Common Dry Bean Production in California: Second Edition (Publication 8402).

Lovatt, C.J., 2001. Properly Timed Soil-applied Nitrogen Fertilizer Increases Yield and Fruit Size of `Hass’ Avocado. J. Amer. Soc. Hort. Sci. 126, 555–559.

Mayberry, K.S., Hartz, T.K., Valencia, J., 1996. Mixed Melon Production in California (Publication 7209).

Micke, W., 1996. Almond Production Manual (Publication 3364). University of California Agriculture and Natural Resources.

Munier, D., Kearney, T.E., Pettygrove, G.S., Brittan, K., Matthews, M., Jackson, L.E., 2006. Small Grain Production Manual - Part 4 (Publication 8167). University of California Division of Agriculture and Natural Resources.

Mutters, R.G., Thompson, J.F., 2009. Rice Quality Handbook (Publication 3514). University of California Division of Agriculture and Natural Resources.

Newman, J., 2009. Greenhouse and Nursery Management Practices to Protect Water Quality (Publication 3508). University of California Division of Agriculture and Natural Resources.

Norton, M., 2009. Growing Processing Freestone Peaches in California: An Overview (Publication 8358).

Norton, M., 2007a. Growing Processing Cling Peaches in California: An Overview (Publication 8276).



Norton, M., 2007b. Growing Prunes (Dried Plums) in California: An Overview (Publication 8264) [WWW Document]. URL http://anrcatalog.ucdavis.edu/Details.aspx?itemNo=8264

Nuñez, J., Hartz, T., Suslow, T., McGiffen, M.E., Natwick, E., 2008. Carrot Production in California (Publication 7226).

Ramos, D.E., 1997. Walnut Production Manual. University of California Division of Agriculture and Natural Resources.

Smith, R., Aguiar, J., Caprile, J., 1997. Sweet Corn Production in California (Publication 7223). Smith, R., Biscaro, A., Cahn, M.D., Daugovish, O., Natwick, E., Nuñez, J., Takele, E., Turini, T.,

2011a. Fresh-Market Bulb Onion Production in California (Publication 7242). Smith, R., Cahn, M., Daugovish, O., Koike, S.T., Natwick, E., Smith, H., Subbarao, K., Takele, E.,

Turini, T., 2011b. Leaf Lettuce Production in California (Publication 7216). Smith, R., Hartz, T.K., Aguiar, J., Molinar, R., 2011c. Chile Pepper Production in California

(Publication 7244). Strand, L., 2008. Integrated Pest Management for Strawberries, 2nd Edition, 2nd Edition edition.

ed. University of California Division of Agriculture and Natural Resources, 2nd Edition, Oakland, Calif.

Strand, L., 2006. Integrated Pest Management for Potatoes in the Western United States (Publication 3316).

Strand, L., 1999. Integrated Pest Management for Stone Fruits (Publication 3389). Summers, C.G., Putnam, D.H., 2008. Irrigated Alfalfa Management for Mediterranean and

Desert Zones (Publication 3512). Turini, T., Cahn, M.D., Cantwell, M., Jackson, L., Holgate, S.T., Natwick, E., Smith, R., Subbarao,

K., Takele, E., 2011. Iceberg Lettuce Production in California (Publication 7215). Wolstenholme, B.N., 2004. Nitrogen – the manipulator element: Managing inputs and outputs in

different environments. South African Avocado Growers’ Association Yearbook 27, 45–61.

CHAPTER THREE


Appendix 3.5 Methods for Estimating NUE

Lead Authors:






Suggested citation:

TS Rosenstock, TP Tomich, H Leverenz, D Liptzin, D Meyer, D Munk, PL Phelan, and J Six. “Appendix 3.5: Methods for Estimating NUE.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.5 METHODS FOR ESTIMATING NUE 2

Appendix 3.5 Methods for Estimating NUE

Two of the most common methods for estimating NUE are the difference method (zero-N) and the isotope dilution method (15N). They are calculated with equations 1 and 2, respectively:

(1) where UF is the amount of N in aboveground biomass measured in a fertilized plot, UO is the N in aboveground biomass in an unfertilized plot, and N is the amount of fertilizer applied. The isotope dilution method applies labeled radioactive N isotopes to determine the amount of plant uptake by the following: (2) where the proportion of 15N in the plant (over background levels) is relative to the 15N fertilizer applied. The principal benefit of utilizing these methods is that they differentiate between N sources - fertilizer or soil reserves. The major limitation is their requirement of controlled experimental plots that may not reflect field-scale N dynamics. The representativeness of prior research to current practices is further suspect because much of the work utilizing these methods in California were performed long in the past (1970s), recent work on rice being an exception. Regardless, these methods provide the most accurate characterization available of fertilizer N recovery efficiency in the state’s crops. In general, zero-N methods tend to overestimate the inorganic N fertilizer recovery and the 15N approach underestimates it (Broadbent et al., 1980). Reference Broadbent, F.E., Tyler, K.B., May, D.M., Moore, C.V., 1980. Tomatoes make efficient use of

applied nitrogen. California Agriculture 6, 24–25.

CHAPTER THREE


Appendix 3.6 Complexity of Industrial N Processes

Lead Authors:






Suggested citation:

TS Rosenstock, TP Tomich, H Leverenz, D Liptzin, D Meyer, D Munk, PL Phelan, and J Six. “Appendix 3.6: Complexity of Industrial N Processes.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.6 COMPLEXITY OF INDUSTRIAL N PROCESSES 2

Appendix 3.6 Complexity of Industrial N Processes

Nitrogen is an essential component in the production of chemicals, plastic, fibers, and fertilizers. But quantification of its use for these purpose is remarkably limited (see Section 3.5 in The California Nitrogen Assessment). The most comprehensive analysis for US industrial N use was completed for 1996 (Domene and Ayres, 2001). For an example of the complexity of N use and variety of uses, see Figure 13, page 100 at: http://onlinelibrary.wiley.com/doi/10.1162/108819801753358517/epdf Reference Domene, L.A.F., Ayres, R.U., 2001. Nitrogen’s Role in Industrial Systems. Journal of Industrial

Ecology 5, 77–103. doi:10.1162/108819801753358517

CHAPTER THREE


Appendix 3.7 N in California’s Solid Waste

Lead Authors:






Suggested citation:

TS Rosenstock, TP Tomich, H Leverenz, D Liptzin, D Meyer, D Munk, PL Phelan, and J Six. “Appendix 3.7: N in California’s Solid Waste.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.7 N IN CALIFORNIA’S SOLID WASTE 2

Appendix 3.7 N in California’s Solid Waste

People, businesses, and municipalities in California dispose of a large quantity of N-rich

materials in landfills. In 2008, organic materials were estimated to be 32.4% of the overall waste

stream (Table 3.7.1). Food, lumber, and leaves and grass were the 1st, 2nd, and 6th most prevalent

materials found with food waste representing the largest fraction (15.5%) or in absolute terms,

5,586,552 Mg (CIWMB, 2009). When only considering refuse from residential homes, the

percentage of food waste increases to 25% of the 2008 waste stream, an increase from 17% in

2004. Other compostable materials, such as leaves and grass, prunings, branches and stumps,

and manures, account for 7.2% of waste.

Construction is another contributor to N accumulation in the waste stream. Even with

the recent precipitous decline in construction activity (CA DOF, 2013), the quantity of

construction and demolition material reaching the landfill is increasing. CIWMB (2009)

estimates that as much as 29% (10.5 million Mg) of the solid waste stream in 2008 was derived

from construction and demolition activities, a 33% increase in the quantity of such materials

since 2004. Fifty percent of this total (14.9% of the total waste stream or 5,230,357 Mg) is derived

from lumber, a 48% increase between 2004 and 2008.

An increasing fraction of organic waste is processed for reuse. Between 2000 and 2008,

there was an 81% increase in the amount of such material processed (Cotton, 2010). Largely the

increased processing results from landfill diversion programs but sources of feedstock include:

wastewater treatment plants, municipalities, and agricultural sources as well. The greatest

portion of materials in 2008 was derived from individuals who self-haul (28%) and commercial

sources (25%).

Processors and composters produce a small number of products (less than 5) that are

distributed and applied to land. Agricultural, landscape, and nursery are the principal sinks for

recycled organic wastes, accounting for 46% of the total across five regions. More than 26% of

the organic materials are used at landfills (beneficial reuse and alternative daily cover). Regional



differences in reuse are apparent (Table 3.7.2). In Southern California, 50% of the materials are

spread as alternative daily cover at landfills where it is placed on the surface of refuse to control

nuisance (e.g., blowing litter and odor). In contrast, nearly half (48%) are recycled to agricultural

soils in the Central Valley. Differential reuse determines the reentry of N into California N

cycle. The N dynamics and fate will change with application.

So, there are data to understand ‘who’ and ‘what’ is being disposed of as solid wastes. But

an equally important question to understand N flows is ‘where’ the solid waste and imbedded N

originated. Was the material transferred into California representing an importation and

concentration of N in the state? Or was the material mobilized from within California?

Answering such questions requires greater resolution on the materials being landfilled and

assumptions about the distribution of production which are not realistic to make at this time.

Still, with the local importance of landfills and solid waste on N cycles, these types of question

may deserve more attention in the future.

Table 3.7.1. Composition of California’s solid waste stream: 1999, 2003, and 2008. Sample numbers were 1682, 550, and 751 in the three years respectively. Much of the solid waste disposed of in landfills contains nitrogen (N), raising concerns for N2O and NO3

- emissions. Despite becoming a lesser percentage of total waste stream, the absolute amount of organic waste disposed of in landfills has remained relatively constant between 1999 and 2008. Food represents a significant fraction of this waste. Source: CIWMB, 2009. 1999 2003 2008 Material Est. % Est. Mg Est. % Est. Mg Est. % Est. Mg Paper 30.2 9,743,635 21 7,660,512 17.3 6,221,223 Glass 2.8 917,377 2.3 849,792 1.4 513,221 Metal 6.1 1,962,821 7.7 2,825,629 4.6 1,641,383 Electronics 1.2 436,587 0.5 196,181 Plastic 8.9 2,867,672 9.5 3,455,397 9.6 3,453,812 Organic 35.1 11,328,585 30.2 11,034,972 32.4 11,689,451 Construction & Demolition 11.6 3,728,247 21.7 7,919,991 29.1 10,501,036 Household Hazardous Waste 0.3 96,593 0.2 66,754 0.3 109,522 Special Waste 3.1 1,007,117 5.1 1,848,857 3.9 1,402,648 Mixed Residue 1.8 578,610 1.1 396,765 0.8 300,118



Table 3.7.2 Regional distribution and use of composting and processing products (Mg) in 2008. Distribution of organic wastes to land represents an important recycling of nitrogen (N) into the California’s N cycle. Based on the recent survey of composting and processors, agriculture and landfills are the primary sinks for recycling organic waste. Source: CalRecycle, 2010. Region Use Bay Area Central Coast Central Valley Northern Southern Agricultural 358,323 413,365 1,534,508 52,048 462,726 Landscape 306,610 97,371 284,656 32,022 461,169 Nursery 60,656 1,768 93,330 1,947 232,499 Caltrans 4,740 16,484 11,385 Alternative Daily Cover 83,694 10,807 140,572 3,131 2,124,637 Biomass Fuel 399,840 56,258 1,014,725 37,858 652,805 Municipal 4,843 4,845 765 10,383 Beneficial Reuse at Landfills 20,425 27,906 174 108,215 Other 112,296 556 88,100 208,297 Total 1,341,844 617,614 3,177,219 127,944 4,272,115

References

CA DOF, 2013. Historical County and City Estimates [WWW Document]. Reports and Research Papers. URL http://www.dof.ca.gov/research/demographic/reports/view.php#objCollapsiblePanelEstimatesAnchor (accessed 6.6.15).

CalRecycle, 2010. Third Assessment of California’s Compost- and Mulch-Producing Infrastructure -- Management Practices and Market Conditions (No. DRRR-2010-007).

CIWMB, 2009. California 2008 Statewide Waste Characterization Study (IWMB-2009-023). CalRecycle (California Department of Resources Recycling and Recovery).

Cotton, M., 2010. Integrated Waste Management Consulting [WWW Document]. URL http://www.mattcotton.com/

CHAPTER THREE


Appendix 3.1 Average N Fertilizer Application Rates by Crop, 1973 and 2005





Suggested citation:

K Thomas, D Liptzin, TP Tomich, M Coley, R Dahlgren, B Houlton, K Scow, and A White. “Average N fertilizer application rates by crop, 1973 and 2005.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.



3.1 Average N fertilizer application rates by crop, 1973 and 2005 Area is based on a five-year average centered on 1973 and 2005. The average nitrogen (N) application rate has only increased 25% over 33 years. However, the magnitude and direction of change is crop specific. Four of the thirty-three commodities use more than 50% of total N use accounted for in this analysis: almond, cotton, rice, and wheat. Source: Rosenstock et al. 2013. Area (ha) N rate (kg / ha)

Δ N rate (%) N (% total)

Crop 1973 2005 1973 2005 1973 2005 Almond 86462 236800 142 201 41 6 15 Avocado 8144 24728 140 125 -11 1 1 Beans, dry 67760 25600 57 102 79 2 1 Broccoli 17432 47000 204 213 4 2 3 Carrots 12592 28248 134 242 80 1 2 Cauliflower 9264 13624 205 267 30 1 1 Celery 7220 10296 321 290 -10 1 1 Corn, sweet 5680 10224 162 239 47 0 1 Cotton 372840 250400 122 195 60 24 16 Grapes, raisin 96080 96000 64 49 -23 3 2 Grapes, table 26432 33280 64 49 -24 1 1 Grapes, wine 65992 191120 59 30 -49 2 2 Lemons 16608 19360 186 138 -26 2 1 Lettuce 58048 92960 178 216 21 5 6 Melons, cantaloupe 19016 17840 106 182 71 1 1 Melons, watermelon 4480 4768 178 169 -5 0 0 Nectarines 4184 13480 147 116 -21 0 1 Onions 11400 18744 164 237 45 1 1 Oranges 74416 76960 73 106 46 3 3 Peaches, clingstone 20200 11752 149 114 -23 2 0 Peaches, freestone 8440 13360 149 127 -15 1 1 Peppers, bell 3520 8280 181 388 114 0 1 Peppers, chili 1887 2184 181 336 85 0 0 Pistachio 41040 166 178 7 2 Plums, dried 33120 27040 106 146 37 2 1 Plums, fresh 9416 12880 123 116 -6 1 0 Potato 28024 16328 212 278 31 3 1 Rice 165200 214320 96 146 52 8 10 Strawberry 3448 13472 178 216 21 0 1 Tomatoes, fresh market 11272 15520 159 198 24 1 1 Tomatoes, processing 88776 111760 159 204 28 7 7 Walnut 63616 86080 134 154 15 4 4 Wheat 270240 157920 99 198 101 14 10 Average 145 181 25

CHAPTER THREE


Appendix 3.2 Are University Nitrogen Rate Guidelines Current?





Suggested citation:

K Thomas, D Liptzin, TP Tomich, M Coley, R Dahlgren, B Houlton, K Scow, and A White. “Are University Nitrogen Rate Guidelines Current?” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 3.2 ARE UNIVERSITY NITROGEN RATE GUIDELINES CURRENT? 1

3.2 Are University Nitrogen Rate Guidelines Current? Since World War II and continuing to the present day, University of California (UC) research has established crop-specific “Nitrogen (N) rate guidelines” (Proebsting 1948; Hartz and Bottoms 2009). An N rate guideline is a range of N application rates expressed as a unit of weight area-1 (e.g., kg ha-1) that are generally able to achieve maximum yield. Most often, N application rate guidelines are printed in University of California Department of Agriculture and Natural Resources (UC DANR) publications and are extended to producers through information channels including: bulletins, production manuals, and field days. The California Nitrogen Assessment analyzed the current status of N rate guidelines for 33 major commodities grown in California and found publications from UC DANR with N guidelines published within the last 25 years for 28 of the 33 crops (Appendix 3.3). Guidelines for 58%, 64%, and 86% of the 28 commodities had been published within the last 5, 10, and 15 years, respectively. In most cases, more recent publications were revisions of previous guidelines to incorporate new research, changes in management practices, and crop genetics.

Current N guidelines vary widely between their lowest and highest values (Table A3.2). The minimum suggested application rate is often almost 100% less than the maximum rate for any single commodity. The large range can be justified by the diversity of cropping systems, technologies, and growing conditions for any one commodity. The combination of which can create large differentials in crop demand, system efficiency, and the amount of fertilizer demand. When comparing current estimated N application rates with the guidelines, the estimated current rates were above the guidelines for 45% of the 33 crops, and within the guidelines for 55% of the crops. For those estimates that were within the guidelines, 31% were in the top quartile of the guideline. These findings suggest either the guidelines underestimate the N required or average producers over-apply N fertilizer for a considerable number of crops in California. TABLE A3.2 Comparison of average 2005 fertilizer nitrogen application rates to University guidelines. The comparison provides a measure to determine if average nitrogen (N) application rates are within that suggested by research results. Application rates that exceed the maximum in the guidelines suggest that either the guidelines do not reflect cropping conditions or growers over-apply N. 1The percentage of crops with an average N application rate within the UC guidelines. 2The percentage of crops with an average N application rate exceeding the maximum listed in the UC guidelines. 3The amount of N applied above the maximum rate in the guidelines.

Crop type N Range of guideline

(% ± SD) Within1

(%) Over2 (%)

Mean surplus3 (lbs. N per acre± SD)

Field crops 4 73 ± 46 100 - - Perennials 12 88 ± 54 50 33 14 ± 12 Vegetables and annual fruits 12 101 ± 83 58 42 53 ± 47 All crops 28 90 ± 65 57 36 36 ± 39

CHAPTER FOUR

A California Nitrogen Mass Balance for 2005

Appendix 4.1 Supplemental data tables for Chapters 3, 4, and 5

Authors:

D. LIPTZIN, T.S. ROSENSTOCK, V.R. HADEN, B.L. YEO



Suggested citation:

D Liptzin, RA Dahlgren, and T Harter. “Appendix 4.1: Supplemental data tables for Chapters 3, 4, and 5.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 4.1 SUPPLEMENTAL DATA TABLES FOR CHAPTERS 3, 4, AND 5 2

4.1 Supplemental Data Tables

The data tables demonstrate that the amount and quality of available information on the causes, states, and consequences of nitrogen (N) cycling in California varies. Multiple data sources or methods were available in some cases. However, often conclusions had to be made based on limited and incomplete information. Improvements to the following areas would greatly advance our ability to track N flows and better understand the historical and future impacts of a changing N cycle on California.

1) Collect inorganic and organic N fertilizer application rates systematically. Use of N fertilizer represents the largest flow of new N into the environment, but the system of reporting is flawed and incomplete. In addition, application of organic N sources is not tracked (except for the San Joaquin Valley dairies). One opportunity may be to couple or model a N fertilizer reporting system based on the pesticide reporting system. The cost of compliance and burden of reporting needs to be considered if enhanced reporting is implemented.

2) Create a real-time updating system to integrate data collected and monitor changes. Many of the

state agencies (Air Resources Board, California Department of Food and Agriculture, State Water Resources Control Board) collect data relevant to understanding the N challenge in California. However, the data are either buried on their websites, on the hard drives of a single employee, or only available as hard copies in their regional offices. A system that compiles data from various agencies and tracks changes in patterns would serve as a foundation for integrated cross media (air, soil, and water) responses.

3) Emissions, and the factors controlling their variability, need to be better established. In particular,

information on leaching from croplands under current conditions, ammonia emissions from tailpipes, biological N fixation in crop and natural lands, and nitrous oxide emissions present significant uncertainty. Additional research to better characterize emissions from these sources will help researchers, farmers, and policy makers devise more targeted solutions.

4) Ground level concentrations of NOx, NH3, O3 and PM are detectable by satellites, which are

being used in conjunction with surface data and meteorological models to give a more complete assessment of spatial trends. Despite the advantages of better geographic coverage, the main limitation of remotely sensed data is that they lack the continuous temporal resolution. The value of remote sensed data for monitoring and regulatory compliance is expected to increase as long-term satellite records accumulate over time and more sophisticated air quality models for integrating these data with surface measurements are developed.

Introduction The following data tables supplement and support the statements and conclusions in the body of the assessment report. The data tables are not summaries of findings, but rather summaries of what is known and available to evaluate the causes, states, and consequences of nitrogen (N) cycling in California. To this



end, the data tables have two primary purposes: (1) summarize the sources of data and approaches used in the assessment, and (2) systematically evaluate the quality of available data. The data tables are organized by broad categories and each includes the indicators used in the assessment related to that topic. Relevant sections of the assessment are noted for each indicator in the table to track information and show linkages across chapters. The following topics are covered in the data tables. Table 1: Synthetic fertilizer use Table 2: Industrial synthetic nitrogen Table 3: Biological nitrogen fixation: natural lands Table 4: Biological nitrogen fixation: cropland Table 5: Nitrous oxide (N2O) and dinitrogen (N2) gas Table 6: Nitric oxide (NO) + nitrogen dioxide (NO2) and ammonia (NH3) Table 7: Area burned by wildfire Table 8: Land use Table 9: Nitrogen storage Table 10: Harvested nitrogen Table 11: Animal production Table 12: Organic nitrogen reuse Table 13: Agronomic nitrogen use efficiency of crops Table 14: Solid waste Table 15: Manure Table 16: Groundwater nitrogen Table 17: Surface water nitrogen Table 18: Dissolved nitrogen in waste discharge Table 20: Nitrogen in drinking water Table 21: Nitrous oxide emissions from agricultural soils Table 22: Nitrogen-related air pollutants



Following the models of other assessments, “reserved wording” was used to quantify areas of uncertainty (Box 1; modified from (Ash et al., 2010)), providing a more consistent analysis across chapters. This approach takes into account both the level of scientific agreement and amount of available evidence. Box 1 Communicating uncertainty (Source: Ash et al. 2010)

Quantitative Analyses – the following reserved wording was used for statements that lent themselves to formal statistical treatment, or for judgments where broad probability ranges could be assigned:

Virtually certain Greater than 99% chance of being true or occurring Very likely 90-99% chance of being true or occurring Likely 66-90% chance of being true or occurring Medium likelihood 33-66% chance of being true or occurring Very unlikely 1-33% chance of being true or occurring Exceptionally unlikely Less than 1% chance of being true or occurring

Qualitative Analyses – the following reserved wording was used for more qualitative statements:

Amount of Evidence

Limited Medium High

Leve

l of A

gree

men

t High Agreed but unproven Agreed but incompletely documented

Well-established

Medium Tentatively agreed by most

Provisionally agreed by most

Generally accepted

Low Suggested but unproven Speculative Alternate explanations



Table 1. Synthetic fertilizer use

Indicator Data sources and approach Statewide nitrogen (N) fertilizer use (Sections 3.2, 4.1.2; Appendix 4.2.4; Figure 3.2; Box 4.3)

Compilation of fertilizer sales data from annual tonnage reports for 2002-2007 (California Department of Food and Agriculture). Sales data for years following 2002 were deemed unreliable due to unexplainable 50% increase in sales. Mass balance calculations used mean value for 1980-2001.

Fertilizer use by crop (Sections 3.2.1, Appendix 4.2.2,; Table 3.1; Figures 5.1.3, 5.1.5; Appendix 3.1)

Estimated fertilizer use for the known land cover types receiving fertilizer was summed. With the exception of turfgrass, fertilization rate was multiplied by the acreage. • The acreage of cultivated crops was calculated as the mean acreage

reported in the USDA National Agriculture Statistics Service (NASS), California County Agricultural Commissioners’ Data (2002-2007) annual summary of statewide data. The crops were aggregated to match the categories used by the California Department of Water Resources (DWR) land use surveys. The fertilization rates were calculated as the average of the recommended rates across all regions and management practices in the recent (1999-2010) UC Davis Cost Studies and the grower reported rates for California in the USDA Chemical Use Surveys.

• The acreage of environmental horticulture crops was based on the average of the 2002 and 2007 USDA Agricultural Census. Fertilization rates were calculated based on expert opinion (R. Evans, UC Davis) of irrigation rates and fertilizer concentrations.

• Turfgrass fertilizer use was based on expert opinion (B. Augustin, Scotts-Miracle Gro Company) and was scaled down from the national estimate of fertilizer use on turfgrass and the fraction of national turfgrass in California reported by Milesi et al. (2005).

Uncertainty and information status ♦ Statewide fertilizer use is not directly measured, so fertilizer sales are the best available proxy. There

was high agreement between the top-down approach based on fertilizer sales data, compared to the bottom-up approach which calculated fertilizer usage by land cover type (e.g., cultivated crops, environmental horticulture, and turfgrass). At the statewide level, these resulted in estimates of fertilizer use within 5% of each other (ignoring the large reported increase in N fertilizer sales starting in 2002).

♦ For the top-down approach, the CDFA’s annual tonnage reports provide a better estimate of the tonnage of fertilizing materials sold than of the tonnage of N sold (see Box 4.3 in CNA). It appears that problems in the reporting system may explain the puzzling 50% increase in reported sales from 2001 to 2002.

• There are potentially double counting problems in the accounting methodology. The reporting system is designed to track the amount of fertilizing materials sold by licensed



dealers to unlicensed purchasers by county. However, it is possible that some dealers are reporting all sales.

• The largest source of error is likely the conversion of the tonnage of materials to tonnage of N for farm use fertilizers. While the common fertilizers have a specific grade (e.g., the grade of anhydrous ammonia is 82-0-0 or 82% N, 0% phosphorus and 0% potassium), in California there is a large tonnage of specialty fertilizers that are lumped together as “other.” In older tonnage reports the “other” category was assumed to have a grade of 10-3-3, but the current tonnage reports do not state this explicitly.

• Reporting of tonnage of non-farm fertilizing materials is also inadequate as there is no way to report the grade of this material. The tonnage reports do not indicate how the tonnage of non-farm N is calculated, although it can be assumed the grade for these materials is also 10-3-3.

♦ For the bottom-up approach, there is uncertainty in both the acreage and the fertilization rates, with the level of uncertainty dependent on the crop.

• There is high agreement on the acreage of most crops in the various data sources. Acreage reported by the NASS California Agricultural Commissioners’ Data is very similar to the acreage reported in the USDA Agricultural Census.

• However, calculations of fertilization rates are hindered by limited and inconsistent data. Fertilization rates were estimated as the average of the recommended rates reported in the UC Davis Cost Study reports and fertilization rates reported by growers as part of the USDA Chemical Use Surveys. While the rates in these two sources across all crops are highly correlated, they can disagree by 50% for a given crop.

• Turfgrass acreage is calculated based on the empirical relationship observed between impervious surface area and turfgrass from remote sensing imagery. There are no other reliable quantitative estimates of turfgrass acreage for California.

• There is limited evidence for fertilization rates in turfgrass. Our estimate of fertilizer use is based on scaling down the Scotts Company national estimate of fertilizer use on turfgrass. While the Scotts Company does extensive research on the “Do it yourself” homeowners market to evaluate its market share, the total use of fertilizer on turfgrass is suggested but unproven.

• There is medium agreement on the acreage of environmental horticulture crops, but there is limited evidence for the fertilization rates of these crops. This category comprises a variety of crops ranging from woody perennials to annual bedding plants to cut flowers grown in the open. While these highly productive crops very likely receive the highest fertilization rates of any crop in the state, there are no available recommendations or surveys on fertilization rates for any of these crops in California. Further, it is unknown how much recycling of N in the irrigation water occurs.

♦ Estimating fertilization rates is complicated by the large amount of manure produced in the state. More likely than not, manure N replaces synthetic fertilizer as the source of nutrients for many acres of forage crops near dairies. However, it is not clear if this replacement is complete or if these crops still receive some inorganic fertilizer. Further, a large fraction of solid manure appears to be composted to some degree and applied as an organic amendment to soils and not included as part of nutrient management plans.



♦ See also manure (Table 15)



Table 2. Industrial synthetic nitrogen

Indicator Data sources and approach Ammonia consumption for industrial uses (Sections 3.5, 4.1.1, 4.1.4; Appendix 4.2.4)

Industrial nitrogen (N) consumption is only reported at the national level for the United States. To estimate levels for California, the national estimates by Domene and Ayers (2001), Kramer (2004) , and FAOSTAT (2002) were scaled down based on California’s population reported in the US Census.

Uncertainty and information status ♦ Both the total ammonia (NH3) consumption and synthetic fertilizer N use at the national level are

agreed but unproven. Therefore, there should be high agreement on the total consumption of NH3 in forms other than fertilizer calculated by difference. However, there is low agreement, in part due to annual variation.

♦ Disagreement also arises from a lack of publically available data. Domene and Ayres (2001) attempt to track all of the major industrial end uses of NH3 in the United States. However, this information is derived from private industry sources which are compiled by consulting companies. Individual reports are available for most industrial N-containing compounds, but they cost hundreds to thousands of dollars each and are not typically available in libraries. Thus, it is difficult to assess the quality of these data.

♦ Information on the ultimate fate of these materials after they are produced is scarce. Some of the materials, like nylon carpets, acrylonitrile butadiene styrene (ABS) in automobiles and electronic equipment, are increasingly being recycled. However, the majority of these materials likely still end up in landfills where they decompose slowly.

♦ The available information on NH3 consumption is somewhat dated. This is important because unlike fertilizer consumption which has leveled off recently, the use of NH3 has been growing and continues to grow rapidly (International Fertilizer Institute cited in European N Assessment (2011)).

♦ See also solid waste (Table 14).



Table 3. Biological nitrogen fixation: natural lands

Indicator Data sources and approach Biome specific N fixation rates (Sections 3.2.2.3, 4.1.2, 4.1.6; Appendix 4.2.3)

Estimation of natural land nitrogen (N) fixation was based on the dataset presented in Cleveland et al. (1999). The low percent cover estimate for biome specific N fixation rates was used and biome areas were derived from our land cover map (see Table 8). Semi-quantitative estimates of species-specific rates of N fixation are available in the USDA PLANTS database, but there are limited data on the areal extent of these species precluding usage of this approach.

Empirical relationship with actual evapotranspiration (Sections 4.1.6; Appendix 4.2.3)

The statewide average evapotranspiration (ET) was calculated from MODIS data (provided by Qiaozhen Mu, University of Montana) and the empirical relationship between actual ET and N fixation as described in Cleveland et al. (1999).

Uncertainty and information status ♦ Biological N fixation is the largest source of N to natural ecosystems, but magnitude of the rates is

speculative. Two main approaches have been used for symbiotic N fixation: (1) biome specific rates based on Cleveland et al. (1999) and (2) species specific rates. Sobota et al. (2009) used both methods for Central Valley watersheds and found the latter to be 50% less than the former assuming that Ceanothus spp. were the only N fixers.

♦ Biome specific N fixation rates are the most common approach for mass balance studies, but the rates are speculative.

• The biome-specific rates reported in the global data in Cleveland et al. (1999) are likely biased upwards because studies of N fixation are more likely to occur in areas with higher N fixation rates. While these authors report estimates of rates across a range of percent cover for the N fixing species, even the lowest coverage may overestimate fixation rates. For example, two of the seven reported rates of symbiotic N fixation are for Alnus (alder) forests which have very high rates of N fixation.

• The biome-specific estimates of N fixation varied by a factor of three and the estimate based on ET varied by a factor of six, depending on the assumption of percent cover of N fixing species. There was considerable overlap in the range of N fixation estimates between the two methods, but the lower bound for the ET-based estimate was considerably lower. Perhaps because of the Mediterranean climate, the ET for the biomes in California is lower than the global average ET for these biomes. The mass balance approach (subtracting all known outputs from atmospheric deposition) was also on the low end of the range. Because the biome-level estimates do not take into account the quantity of N deposition, this approach assumes that atmospheric N deposition is largely replacing N fixation as a source of new reactive N. One problem with this approach is that the distribution of N fixing species may not align with the distribution of N deposition. That is, regions with high N deposition may be regions that previously had modest rates of N fixation.

♦ Calculations using a species-based approach are currently difficult because of lack of data on the



coverage of individual species. • Plants with symbiotic N-fixing associations are concentrated in a few families and genera:

Fabaceae, Alnus (Betulaceae), Ceanothus (Rhamnaceae), Purshia and Cercocarpus (Rosaceae). While these species have the potential to fix N, relatively few field measurements have been made.

• Very high rates (100 - 200 kg N ha-1 yr-1) have been estimated for pure stands of red alder (Alnus rubra) in the Pacific Northwest, but pure red alder stands are limited in California.

• Many understory species in forests and shrubs in dryland ecosystems are capable of N fixation, but the rates may be low because of moisture and light limitation and low primary productivity. However, one shrub species, Ceanothus velutinus, has been measured to have relatively high fixation rates (>10 kg N ha-1 yr-1) at moderate cover in the forest understory (Busse, 2000) or in the understory of drier coniferous forests (Johnson, 1995). Other Ceanothus species have been measured to fix N at rates up to 100 kg N ha-1 yr-1 in pure stands as well (Conard et al., 1985). Little is known about fixation rates for many of the other shrub species or the predominantly herbaceous, but widely distributed, Fabaceae species in California.

♦ Using the biome-specific rates requires a measure of the areal extent of the biomes. While there are several potential data sources, there is generally high agreement once the detailed vegetation communities are lumped into biomes.

♦ There is limited evidence for the percent cover of most N fixing species. There are vegetation plots which have been inventoried by state and federal agencies or non-governmental organizations that are representative of large areas of the state. However, extracting the appropriate cover data for this analysis was beyond the scope of the assessment.

♦ There is a high degree of uncertainty on the effect of atmospheric N deposition on the rates of N fixation. It is tentatively agreed by most that increasing N deposition will decrease the competitive advantage of symbiotic N fixing species. Fertilization experiments have shown that increased N availability is associated with decreased N fixer abundance (e.g., (Suding et al., 2005)). However, there is limited evidence that N deposition has caused changes in the abundance of N-fixing plant species. Based on the mass balance, we estimate that N deposition is now similar in magnitude to natural lands N fixation. If this increased deposition has resulted in a decrease of N fixation, either by decreasing the abundance of N fixing plants or decreasing the fixation rate without altering abundance, then N fixation would be significantly lower than our estimate.

♦ The presence of non-native species is another factor that may affect natural lands N fixation rates. Some of the plants included in the USDA PLANTS database are crop species (e.g., Medicago sativa and Trifolium spp.) and most others were deliberately introduced for various reasons (e.g., Vicia spp. and Eleagnus angustifolia, Cytisus scoparium). These species tend to fix at high rates with twelve in the “high fixer group” and eighteen in the “medium fixer group,” out of thirty-four species total. There is limited evidence for the magnitude of N fixed by these species, but they are widespread in many ecosystems and especially in human disturbed ecosystems. While these species may change local ecosystem nutrient cycling, they likely do not change the statewide N balance. For example, if we assume a medium fixation rate (120 kg N ha-1 yr-1) and 5% cover of non-native N fixers on 1 million ha, the statewide total would only be 6 Gg N yr-1.



Table 4. Biological nitrogen fixation: cropland

Indicator Data sources and approach Crop specific nitrogen (N) fixation rates (Sections 3.2.2.3, 4.1.2; Appendix 4.2.3; Figure 3.5)

The N fixation rates were multiplied by the mean crop acreage for 2002-2007 reported in the USDA National Agricultural Statistics Service (NASS), California Agricultural Commissioners’ Data.

• For alfalfa, the empirical relationship reported by Unkovich et al. (2010) between N fixation rates and plant productivity was used.

• For all other crops, the standard literature value of N fixation rates was used (Smil, 1999).

Uncertainty and information status ♦ N fixation has not been quantified in California alfalfa for several decades. However, the reported

relationship between N fixation rate and dry matter production reported in Unkovich et al. (2010) is quite robust. While the rates of productivity are significantly higher in California (i.e. the statewide mean productivity is higher than the highest value reported by Unkovich et al.), there is no reason to think that the relationship between dry matter production and N fixation would differ at higher productivity levels. This relationship, however, is only for aboveground biomass.

♦ For alfalfa, the most important source of uncertainty is the amount of fixed N in belowground biomass. Unkovich et al. (2010) suggest that 50% of total N fixation has been reported to be in belowground biomass. Because of the frequent number of cuttings (up to 12 in the Imperial Valley), root production likely lags behind aboveground production. Thus, we assumed that only 25% of total fixation would be in belowground biomass. The difference in assumptions about fixed N in roots represents a difference of 83 Gg N yr-1.

♦ While there is limited evidence for the fixation rates of leguminous crops other than alfalfa, they cover such small acreages that total crop fixation rates are not affected. Two possible exceptions include:

• Irrigated pasture used to be managed to have high clover cover. Based on expert opinion (M. George, UC Davis), we assumed a value of 10% clover cover in irrigated pasture, a value which is suggested but unproven.

• The growing conditions for rice also favor the growth of several taxa of blue-green algae. Based on Smil (1999), N fixation rates by these free living N fixers (e.g., Nostoc and Anabaena) are 20-30 kg N ha-1 yr-1. However, N fixation has not been measured in rice in California in recent decades. While these taxa occur, in some cases they are managed because they compete with rice seedlings. Even though there is limited evidence to support both rice and pasture N fixation, the maximum error is likely less than 10 Gg N yr-1 combined.



Table 5. Nitrous oxide (N2O) and dinitrogen (N2) gas

Indicator Data sources and approach Fossil fuel combustion N2O: emissions inventory (Sections 3.4, 4.1.1, 4.1.7; Tables 4.1A, 4.13; Appendix 4.2.1)

All fossil fuel combustion related emissions of N2O were based on the average of the 2002-2007 California Air Resources Board (ARB) Greenhouse Gas Emission Inventories.

Natural land soils N2O: biome-specific rates (Sections 4.1.1, 4.1.6; Table 4.5; Appendix 4.2.8)

For natural land emissions, the biome-specific rates from each published source (see Table 4.5 in CNA) were multiplied by the biome areas in the land cover map (see Table 8 below). The mean of the estimates from all sources was calculated.

Cropland soils N2O: literature compilation (Sections 4.1.1, 4.1.2, 4.2.8; Table 4.5; Appendix 4.2.8)

For cropland emissions, the mean was calculated for the product of the area of cultivated cropland in the land cover map (see Table 8) and published global estimates of cropland rates (see Table 4.5 in CNA) as well as the median rate for California (see Appendix 4.2.8).

Soil N2 emissions: N2:N2O ratios (Sections 4.1.1, 4.1.7; Appendix 4.2.8)

The reported N2:N2O ratios for cropland and natural land in Schlesinger (Schlesinger, 2009) were used to estimate N2 emissions.

Aquatic ecosystems N2O and N2

(Section 4.1.8; Appendix 4.2.9)

In aquatic systems, both N2O and N2 were estimated independently based on published estimates in Beaulieu et al. (2011) and Mulholland et al. (2009).

Groundwater denitrification (Section 4.1.9; Appendix 4.2.10)

Denitrification was estimated with three approaches: (1) assuming a half-life of nitrate of 31 years (Green et al., 2008) and historical estimates of N inputs to groundwater, (2) multiplying estimates of the volume of groundwater (CA DWR 2003) by an estimated denitrification rate (Liao et al., 2012), and (3) assuming a fixed fraction of N inputs to groundwater is denitrified (Leip et al., 2011; Seitzinger et al., 2006).

Uncertainty and information status ♦ Emissions of N2O from fossil fuel combustion are tentatively agreed upon by most. Emissions are

largely derived from fuel use which is well established, but there is limited evidence for the magnitude of the emission factors used.

♦ It is suggested but unproven that N2O emissions from natural land soils are of similar magnitude to cropland soils in California. We rely on several global estimates of N2O emissions for our



calculations, as there are very few field-based estimates across the wide range of ecosystems in the state. Emissions could be higher in California because of the higher amounts of N deposition in many natural land regions. For example, Fenn et al. (1996) report rates almost an order of magnitude higher at a high N deposition site compared to a low deposition site.

♦ Rates of nitrous oxide emissions from soils are provisionally agreed upon by most. Of the four calculated estimates, the median value of California field data was more than double the other estimates, but may be high for two reasons:

• First, the crops that were examined may be fertilized at higher rates than the average crop in the state.

• Second, in many cases, the timing of the measurements may not have been ideal for scaling up to an annual scale. Stehfest and Bouwman (2006) note that the length of the study is negatively related to N2O fluxes. That is, the longer the time span that was included in the study, the lower the reported N2O emissions.

♦ Ongoing research in California as part of a concerted effort funded by the California Air Resources Board, California Department of Food and Agriculture, the California Energy Commission, and CalRecycle suggests that with proper management, N2O emissions from well-managed California crops can be significantly lower than the global averages. In addition, this project is helping to develop California-specific parameters to improve the DeNitrification-DeComposition (DNDC) model used by Li et al. (1996). Decreasing this uncertainty is potentially important for estimating greenhouse gas emissions, but is a minor source of absolute uncertainty in the N mass balance calculations.

♦ There is limited scientific evidence for dinitrogen (N2) emissions from soils. This is one of the largest sources of uncertainty in many N budgets for natural lands. Schlesinger (2009) compiled existing data, but there are still relatively few estimates of N2 emissions and little understanding of what controls the emission rates. There are two main methods for field measurements of N2: (1) incubations with acetylene, and (2) 15N labeling experiments. In the former, the last step of denitrification, the conversion of N2O to N2, is blocked and N2O is measured. For the latter, the labeled N can be measured directly as N2. There is a growing consensus that isotopic methods are more robust, but they are more time consuming and expensive to use. Ecosystems models like DAYCENT and DNDC can estimate N2

emissions, but need to be validated with field measurements. ♦ Pioneering work on denitrification using the acetylene method was conducted in agricultural soils in

California in the 1970s and 1980s although denitrification in soils was studied even earlier by Broadbent (1951). The Schlesinger (2009) database included three of the published studies, but excluded Ryden and Lund (1980). These four works found higher denitrification rates as well as a lower ratio of N2O to total denitrification. However, the N input rates in these studies were relatively high (300 - 680 kg N ha-1 yr-1). Even using the higher N2:N2O ratio from the average of these four California studies, the amount of N emitted as N2 from cropland soils would only increase from 17 to 52 Gg N yr-1, suggesting that leaching still dominates over gaseous emissions for N outputs from cropland soils.

♦ N2O and N2 emissions might differ in California’s Mediterranean climate due to timing of N availability compared to many other temperate ecosystems. During the hottest months of the years, biotic gas fluxes are minimal as the soils are dry and there is little biological activity. N availability in soils and N concentrations in surface water in natural areas tend to be highest in the fall at the time of



the first rain (Ahearn et al., 2004). This is a time of year when many plants are relatively inactive because of hot and dry conditions.

♦ Estimates of statewide wastewater N2O emissions are speculative due to: (1) a lack of compiled data on the level of treatment, and (2) a wide range of values dependent on the processes used at each facility.

• The State Water Resources Control Board conducts a wastewater user charge survey annually, which asks about treatment level. However, this database is collected by the wastewater agency rather than the facility, making it difficult to assign a treatment level and thus the N2O emissions for each facility.

• One study, Czepiel et al. (1995), conducted for one spring and summer at one treatment plant in Durham, New Hampshire, forms the basis of the emission factor used by the ARB for the California Greenhouse Gas Emission Inventory as well as the EPA’s National Greenhouse Gas Inventory, as well as one option for Tier I estimates for the International Panel on Climate Change.

• Foley and Lant (2008) compiled the few literature values reporting the fraction of influent N released as N2O. They report a range spanning two orders of magnitude and a median value of 0.01 kg N2O- kg -1 influent N. That is, 1% of influent N is converted to N2O. For California, the per capita and influent concentration approaches both result in 2 Gg N2O-N emitted per year.

♦ N gas emissions from denitrification in wastewater are not monitored and can only be estimated by difference. Our calculations suggest a N2:N2O ratio of ~17.

♦ N concentration of surface waters varies considerably in space and time (e.g., (Sobota et al., 2009)), making gaseous emissions highly spatially variable. Nitrous oxide emissions from rivers, while relatively low, are related non-linearly to NO3

- concentrations (Beaulieu et al. 2011). Below a threshold concentration of 95 µg N L-1, N2O production was low and insensitive to NO3

- concentrations. However, some rivers in the Central Valley, like the San Joaquin River, have NO3

- concentrations greater than 1 mg NO3

--N L-1 and would be expected to have higher N2O emissions. Thus, using average NO3

- concentrations may underestimate N2O emissions at high NO3- levels and

overestimate N2O emissions at low NO3- levels.

♦ Partitioning the water pixels in land-use maps is difficult because there is no spatially explicit data on the location and size of reservoirs and lakes in California. The Department of Water Resources catalogs the latitude/longitude of dams and the United States Geological Survey maps all water bodies, but these datasets are not always consistent, especially for smaller water bodies.

♦ There is limited evidence for how gas losses differ among rivers, natural lakes, and reservoirs. • Harrison et al. (2008) show that reservoirs differ by an order of magnitude in N retention

calculated as the difference between N inputs and N outputs. By this definition both denitrification and sedimentation are included in retention.

• The total denitrification rates estimated by Seitzinger et al. (2006) were similar between rivers and lakes, but their lakes category included both natural lakes and reservoirs.

• The majority of the lake acreage, and most of the large natural lakes in California, are saline; however, there is limited evidence for whether denitrification rates in saline lakes are similar to either freshwater lakes or coastal areas.

♦ Groundwater denitrification is suggested but unproven. Chemical tracers show that denitrification is



widespread even in portions of the aquifer that are oxygenated. However, calculating rates of denitrification over large areas is very difficult because of heterogeneity in N inputs, geochemistry, and hydrology. The approaches vary in their scale, but there is limited evidence, but high agreement, when applied to California.

• Techniques such as measuring N2: Argon ratios and dual isotopes of NO3- can be used to

estimate the fraction of initial NO3- that has been denitrified. Combined with measurements

of water age, the half-life of N can be estimated; this can be converted into an annual rate. A relatively small number of sites have been sampled with this approach. Further, a historical estimate of N inputs to groundwater is necessary. We assumed a linear increase since 1940 just before synthetic fertilizer use became widely commercially available.

• Transport models can estimate rates of denitrification in mg N L-1. In addition to estimates of N and water dynamics, this approach also requires an estimate of the volume of groundwater.

• The less complex IMAGE model of N dynamics (Van Drecht et al., 2003) applied at the global (Seitzinger et al., 2006) and continental (Leip et al., 2011) scales both suggest that 40% - 50% of N inputs are denitrified.

• Validation of these approaches at the scale of California or even just the Central Valley is not currently possible.



Table 6. Nitric oxide + nitrogen dioxide (NO2) and ammonia (NH3)

Indicator Data sources and approach Fossil fuel combustion: emissions inventory (Sections 3.4, 4.1.1, 4.1.7; Tables 4.1, 4.12; Appendix 4.2.1)

The 2002 United States EPA National Emission Inventory (NEI) for criteria pollutants was used to determine emissions of NOx and NH3 related to fossil fuel combustion. While NH3 is not regulated as a criteria pollutant, it is monitored and reported in the same inventory because it is a precursor to PM2.5. The California ARB also creates an inventory, but we rely on the EPA data because it was used for modeling N deposition.

Soil emissions: biome specific rates (Sections 4.1.2, 4.1.6, 4.1.7,; Table 4.5; Appendix 4.2.8)

Emissions from natural land and cropland soils were based on the product of published rates (see Table 4.5 in CNA) and biome areas. The NH3 emissions were based on data from Potter et al. (2003). The biome areas were derived from the land cover map (see Table 8 below).

Livestock emissions (Sections 3.3.3, 4.1.3, 4.1.7; Figure 3.6; Appendix 4.2.6)

Livestock NH3 emissions were based on livestock populations reported in the USDA Agricultural Census and emission rates in EPA (2004).

Uncertainty and information status ♦ Ammonia and NOx are both estimated largely based on indirect measures (with the exception of

stationary source NOx emissions which are actually monitored at the point of release from the facility) and compiled as part of an emission inventory by the ARB and the US EPA National Emissions Inventory. Uncertainties in these inventories can be due to either uncertainties in the general approach or in the parameter values.

• For example, the ARB and EPA model emissions as the product of an activity level and an emissions factor. Thus, there are uncertainties both in terms of the activity levels (e.g., kilometers traveled per day) as well as the emissions factors (i.e. emissions per unit activity).

• There is also uncertainty associated with choosing which emission processes to include in the model (e.g., running exhaust, idle exhaust, starting exhaust and resting exhaust).

♦ Uncertainties in the NOx inventory vary by sector because of different methodologies. • In many cases, stationary sources, like power plants, are based on actual stack emissions and

are thus the most certain. • Mobile source emissions are agreed but incompletely documented. In general, there is more

evidence for emissions from gasoline vehicles than diesel vehicles. • There is limited evidence for area-wide sources and emissions from natural sources like

wildfires. ♦ It is more difficult to assess uncertainty in NH3 emissions. The ARB methodology has not been

approved for public release yet. The EPA NEI does not use the draft calculations done by EPA for livestock emissions as they have not been formally approved yet either. In general, the relative certainty for NH3 emissions is similar to NOx across the sectors, but overall, NH3 emissions are less certain than NOx emissions.



• For cropland NOx emissions (Matson et al., 1997) and cropland NH3 emissions (Krauter and Blake, 2009; Krauter et al., 2006), the calculations are based on a single study of field measurements conducted over a relatively small area over a short period of time. Similarly, the natural land NH3 measurements (Potter et al., 2003) are based on a single modeling study with limited validation.

• Ammonia measurements are taken infrequently from soils. In addition, they are often measured or modeled from bare soil. However, it is likely that NH3 uptake occurs in plant canopies. Thus, the estimated emissions may be overestimates of the net flux of NH3 to the atmosphere above the boundary layer.

• The livestock NH3 emissions are based on livestock populations and livestock or facility-type specific volatilization rates. While there is high evidence for the populations, there is limited evidence for the NH3 emissions from the various types of animals and types of manure management at facilities. While EPA (2004) methodology attempts to estimate emission rates for the various types of manure management systems, there is limited data on the number of each type of facility in California. Furthermore, because of the variability in emissions rates due to operational processes (e.g., frequency of manure collection) it is insufficient to simply know the type of facility or manure management processes used.

• In the Central Valley, where the Regional Water Quality Control Board regulates almost all dairies, the type of facility can be inferred from the N management plans that are currently submitted to comply with the 2007 Waste Discharge Requirements General Order for Existing Milk Cow Dairies. For example, corral dairies would be expected to differ substantially from freestall dairies in emissions from the amounts of liquid and solid manure that they produced.

• In the ARB inventory for criteria pollutants, it is unclear why there is a large increase in livestock NH3 emissions during the 2000s as the animal populations have changed only slightly over this time period.

• There is limited evidence for emissions of NO from hot desert soils (McCalley and Sparks, 2009). However, this flow is unlikely to be of significant magnitude.

♦ See also manure (Table 15)



Table 7. Area burned by wildfire

Indicator Data sources and approach Total nitrogen (N) volatilization: land based approach (Sections 3.7.3; 4.1.1, 4.1.6, 4.1.7; Figure 4.7; Appendix 4.2.8)

Annual estimates of acreage burned in California, subdivided by vegetation type (e.g., forest, grassland, etc.) (California Department of Forestry and Fire Protection). The typical N volatilization per unit area was based on Johnson et al. (1998).

Emissions of NOx, NH3, and N2O): emissions inventory (Sections 4.1.1, 4.1.6, 4.1.7; Appendix 4.2.8)

Wildfire as a source of NOx and NH3 was estimated from the EPA 2002 National Emissions Inventory of criteria pollutants. Wildfire as a source of N2O was based on the California Air Resources Board Greenhouse Gas Emission Inventory.

Uncertainty and information status ♦ The land area burned by wildfire is agreed but incompletely documented. The database from which

the estimates are derived is compiled from various sources of inconsistent quality. This, however, does not detract from their overall value; the California Department of Forestry and Fire Protection considers these data of “high quality” (FRAP, 2010). The questions of quality only affect variables not of interest in this assessment.

♦ The nitrogenous products of wildfire include NOx, N2O, NH3, and N2. The relative amounts depend on the burn conditions, but the predominant product is N2, which is determined by difference. That is, most sources focus either on a particular gas or on the total N volatilized. The emissions of the inventoried gases are tentatively agreed by most.

♦ Using the estimate of Johnson et al. (1998) for total N volatilization on an areal basis during wildfire, the difference of the total N loss and the sum of the three other gaseous products is N2. In terms of N mass balance, the areal rate of N2 emissions is the crucial term, but is suggested but unproven.

♦ See also nitric oxide + nitrogen dioxide and ammonia (Table 6).



Table 8. Land use

Indicator Data sources and approach Historic land cover and use change: urbanization, agricultural relocation, intensification (Section 3.7; Figure 3.4)

Department of Conservation Farmland Mapping and Monitoring Program (http://www.conservation.ca.gov/dlrp/fmmp), Sleeter et al (2008; Sleeter et al., 2010), and USDA Agricultural Census (1977-2007) were used to determine the extent of agricultural land use change.

Current land use map (Map 4.1)

The mapping was an amalgam of data sources for years ranging from 1997 to 2007 for different land cover types. The natural lands component was based on the vegetation mapping by the California Department of Forestry and Fire Protection. The cropland was based on the land surveys of the California Department of Water Resources surveys, supplemented where necessary with data from the Department of Pesticide Regulation. The urban boundaries were based on the Department of Conservation Farmland Mapping and Monitoring Program.

Uncertainty and information status The extent of land use change is speculative. Multiple efforts document land use change, including: The Department of Conservation’s Farmland Mapping and Monitoring Program, the USDA Agricultural Census, and independent primary research. Each uses a different method of calculating land use – ranging from visual surveys to mail-in surveys and remote sensing – and the conclusions derived from the various data sources differ as well. It appears that the major source of uncertainty arises from collection and categorization errors, as sources categorize land use in various ways and to differential levels of specificity. Identification of trends thus depends on nonstandard categorization of land use that is asymmetrical. The USDA Agricultural Census and the California Department of Conservation Farmland Mapping and Monitoring Program (FMMP) use the National Resource Conservation Service’s farmland categories. However, even when the same categorization is used, a misunderstanding of the categories can lead to erroneous conclusions (Hart, 2003). A second source of uncertainty is that the geographic scale sampled is not uniform across data sources. Notably, the Department of Conservation’s FMMP only surveys approximately 50% of California’s land area.

♦ An important source of uncertainty in land use change for understanding N is the fact that only new remote sensing data are explicitly spatial. The impact of land use change on California’s N cycle will largely reflect climate, soils, and changes which are site specific. Without spatially explicit information on land use change and the new and previous management regimes, the consequences of land use changes are difficult to accurately predict.

♦ A source of uncertainty in the land use map is the timescale. Because the DWR has only surveyed some counties infrequently, there is more than a decade difference between counties in the survey year. Both inter-annual variability in annual crops as well as long-term trends in annual and perennial crops are sources of uncertainty in creating a land use map for a single year.



Table 9. Nitrogen storage

Indicator Data sources and approach Repeat sampling (Section 4.1.2; Appendix 4.2.11)

DeClerck et al. (2003) report changes in total soil nitrogen (N) in cropland over a 55 year period; this was converted to an annual basis assuming a bulk density of 1 g soil cm-3.

Mass balance (Sections 4.1.1, 4.1.2, 4.1.4, 4.1.6, 4.1.8, 4.1.9; Appendix 4.2.7, 4.2.9, 4.2.10, 4.2.11)

N storage in the three land subsystems (cropland, natural land, and urban land) as well as the two water subsystems (surface water and groundwater) was calculated as the difference between inputs and outputs.

Uncertainty and information status ♦ Because of the large stocks of N in soils and vegetation, it is difficult to measure changes in N storage

over short periods of time. However, with repeated sampling over decades, trends in storage can be detected.

• It is provisionally agreed by most that N storage is occurring in cropland ecosystems in California. It is likely that N has been stored in cropland because of measured increases in soil N (e.g., DeClerck et al. 2003).

• While no data have been collected in California, it is well established that turfgrass soils have the capacity to increase N storage for decades (e.g., Raciti et al., 2011). Research in California has demonstrated carbon storage in turfgrass soils though they did not measure N (Townsend-Small and Czimczik, 2010).

♦ For the three land-based subsystems, N storage is determined by mass balance. However, there are several lines of evidence that are consistent with increasing N storage.

• Because of the increased acreage of perennial crops (e.g., Kroodsma and Field, 2006), there is likely N storage occurring in the aboveground biomass in cropland as well.

• Based on ecosystem models, there is limited evidence that carbon is accumulating in natural land ecosystems (Potter, 2010). Because of the strong link between carbon and N, it is very likely that N is being stored as well.

♦ See also solid waste (Table 14), groundwater N (Table 16), and surface water N (Table 17).



Table 10. Harvested nitrogen

Indicator Data sources and approach Crop production (Sections 3.2, 4.1.2, 5.1.1.1; Table 4.4; Figure 5.1.4; Appendix 4.2.5)

Annual surveys of crop acreage and production in California are conducted at the county level by the California County Agricultural Commissioners. These data are compiled at the statewide level by the California office of the USDA National Agricultural Statistics Service (NASS). The USDA NASS also conducts an annual survey of major crops in every state. The former was used in the assessment since it includes all crops, while the latter only includes ~50 commodities. Both the NASS Agricultural Census and California Department of Water Resources (DWR) are additional sources of data for crop acreages, but neither provides crop production data for the majority of California crops.

Crop nutrient content (Sections 3.2.3, 4.1.2, 5.1.1.1; Table 3.1; Figure 5.1.4; Appendix 4.2.5)

Crop nutrient and moisture content are not measured in any comprehensive way in California. The most complete database is the United States Department of Agriculture Crop Nutrient Tool. The tonnage of nitrogen (N) harvested is calculated as the product of tonnage harvested, N content, and dry matter content.

Uncertainty and information status ♦ The NASS annual surveys and NASS statewide compilation of California County Agricultural

Commissioners’ reports are the major sources of information on crop acreage. The two sources show a high level of agreement (R2 = 0.99) when crops are clearly identified. However, there are inconsistencies for several important commodities.

• NASS also conducts a more detailed 5 year Agricultural Census. While this source is considered the most reliable for crop acreage, it does not include crop production statistics for most crops in California.

• A large fraction of the acreage, especially of crops grown for livestock feed, is not clearly identified. For example, the categories used by the NASS statewide compilation of California County Agricultural Commissioners’ reports do not specify the type of hay. In some cases, a more detailed description of hay is provided at the individual county level, however, this information is lost when the data are aggregated at the state level.

• In the annual surveys conducted by NASS all non-alfalfa hay is lumped together, while in the more detailed Agricultural Census that NASS conducts every five years, the non-alfalfa hay is partitioned into tame hay and small grain hay. Because these crops cover such large acreages and production, and N management practices differ among types of hay, it is important to distinguish between types of hay.

• In some cases, there is also a discrepancy between the reported acreage and production. For example, there are more than 60,000 ha in the category “Field Crops, Unspecified,” but only about 10,000 Mg of productivity reported on this acreage which is too low for any harvested crop.

♦ The most comprehensive source of crop nutrient and dry matter content is the USDA Crop Nutrient



Tool. This database is a compilation of many published sources (33 for N and 12 for moisture) and contains information on a wide variety of food and forage crops. While this dataset is fairly comprehensive in the crops included, the data are somewhat dated as the median age of the references is 1984. For grain crops the nutrient and moisture content are well established, but for many fruits and vegetables the data are speculative.

• Changes in nutrient content over time are speculative. For example, Davis et al. (2004) reported a small (6%), but significant, decline in protein content in vegetable crops between the 1950s and the 1990s. It is unclear how the dramatic changes in cultivars and management in the last few decades may have altered crop nutrient and dry matter content.

• The N content of some crops is highly elastic and depends on growing conditions. Luxury consumers will increase uptake when it is available (e.g., at higher rates of N fertilization). Greater uptake can translate into greater concentrations of N plant parts.

• For fresh fruits and vegetables (as well as silage), a bigger source of error may be the dry matter content. For example, the dry matter content of lettuce in the database varies from 4% - 6% which represents a difference of 50%.

• Dry matter content in grain and hay crops is well established. One potential source of error is the stage at which hay crops are harvested. We assumed that all crops were harvested at the mature stage when N content is lowest. For small grain hays like oats and wheat, the database reports values 25% lower at the mature stage compared to earlier cuts, with even larger differences reported for some tame grass hays. The timing of the harvest presents a tradeoff between yield and quality with yield increasing and N content decreasing with maturity. The timing of the harvest may also depend on climate as late spring or early fall rains can push the harvest earlier or later.

♦ The Department of Water Resources land use surveys, used to develop the land use map, do not clearly distinguish among feed crops. For example, over 100,000 ha of field crops and almost 500,000 ha of grain and hay crops are not identified as a particular crop. The lack of specificity in these forage crops makes it difficult to calculate N harvest in the crops, as well as to estimate fertilization rates and other processes on large acreages of California cropland.

♦ Pasture is an agricultural land use whose definition varies by source and it is not always clearly distinguished from rangeland. However, we assume that there is no plant product harvested from pasture.

• For the NASS Agricultural Census, pasture is agricultural land that is managed but could not be planted with crops without improvements. It can support livestock, but is not necessarily grazed at any particular frequency.

• According to the NASS Agricultural Census, approximately 300,000 ha, or 5%, of the 5.5 million ha of pastureland in California is irrigated. This compares to the 376,000 ha of pasture based on our land use map using data from the Department of Water Resources.

• Currently, it is likely that most pastureland is minimally managed, with limited seeding of desirable species like clover and little fertilizer applied.

♦ It is likely that commodity boards and some processors take measurements of their crops, but these data are not publically available. The California Department of Food and Agriculture regularly measures contaminants, like pesticides, on produce, but not nutrient or moisture content.



Table 11. Animal production

Indicator Data sources and approach Animal populations (Sections 3.3.1, 4.1.3, Figure 3.6; Appendix 4.2.5)

The National Agriculture Statistics Service (NASS) Agricultural Census from 2002 and 2007 was used to calculate annual production data for meat producing animals. For dairy cattle and layers, inventory data from the NASS annual surveys were used. Cattle populations are also inventoried at the county level by the California Department of Food and Agriculture (CDFA) and at individual dairy facilities by the Regional Water Quality Control Boards, as well as the CDFA Milk Pooling Branch. Horses are included in the NASS Agricultural Census, but the population very likely only includes working animals. Surveys conducted by the American Veterinary Medical Association (AVMA, 2007) were deemed a better source.

Nutrient content (Section 4.1.3; Appendix 4.2.5)

With the exception of milk, there is no agreed upon nitrogen content values for most animal products; the most comprehensive source is NRC (2003). Another often cited source for animal products in watershed nitrogen (N) budgets is from a conference proceedings (Van Horn, 1998).

Uncertainty and information status ♦ Livestock populations, with the exception of horses, are well established.

• Both the NASS Agricultural Census and the CDFA report cattle populations and the statewide herd size differ by less than 10%.

• In the Central Valley, where more than 90% of the dairy cows are located, the herd size at every regulated dairy is also reported to the Region 5 Water Quality Control Board. Similarly, in Region 8, the Water Quality Control Board collects herd size information by dairy facility.

• Outside these regions, the only source for dairy facility locations (but not herd size by dairy) is from the Department of Food and Agriculture Milk Pooling Branch.

♦ The N content of milk is well established. ♦ The N content of other animal products is tentatively agreed by most. While the N content of food

products is readily available, the N content of the entire animal product, including the egg shell and other inedible portions, are needed. Because of limited evidence, it is difficult to evaluate how much uncertainty there is in N content.

♦ We have assumed that there is no spoilage of livestock feed in California. Therefore, all of the imported grain and harvested feed crops are consumed by animals. There is limited evidence for spoilage rates. A widely cited value is 10% of non-hay, non-silage crops in Jordan and Weller (1996). However, it is not clear how this value was estimated. Because most of the feed crops grown in California are hay or silage crops, only the imported grain corn would be subject to spoilage. However, a 10% spoilage loss of imported feed would represent ~20 Gg N. Further, there is limited evidence for the fate of spoiled livestock feed.



Table 12. Organic nitrogen reuse

Indicator Data sources and approach Manure sales (Section 4.1.3; Appendix 4.2.6)

The tonnage of manure sales from 2002-2007 is included in the annual surveys at the county level by the California County Agricultural Commissioners. These data are compiled at the statewide level by the USDA National Agricultural Statistics Service (NASS). The manure N and moisture content was based on Pettygrove (2010).

Manure composting (Sections 3.2.2, 3.3.3; Table 3.3)

The CalRecycle Solid Waste Information System provides information on the location and types of materials used at composting facilities, but not information on tonnages of materials actually composted.

Uncertainty and information status ♦ The extent of organic nitrogen (N) use as a primary fertilizing material or as a soil amendment is

largely speculative. Sales of organic N products, with the exception of manure, are not tracked and data on distribution and rates of use for all organic N sources are largely unavailable.

• Manure sales are tracked as part of the NASS statewide compilation of California County Agricultural Commissioners’ reports. While the type of manure is not reported, based on location and the close relationship between milk production and manure production, it was assumed that this was solid manure similar to corral scrapings sold from dairy farms. The ultimate fate of this dairy manure is unknown, but it is likely composted to some degree either before or after sale and used as an organic amendment.

• Manure from beef feedlots (Harris Ranch and Imperial County) is very likely composted and sold as fertilizer. Composting facilities require permits from CalRecycle and the facility locations and maximum tonnages of materials are available in the Solid Waste Information System. However, the actual amounts and types of materials passing through these facilities are not tracked. Nor is the destination or use of the composted product tracked.

• Manure applications on cropland, managed as part of Central Valley Dairy is limited to between 1.4 and 1.65 times crop uptake. The amount of manure applied is collected by each dairy for regulatory compliance, but the data are not currently available to the public.

♦ Indirect indicators support the conclusion that organic N is increasingly demanded and available in California. Increased acreage in organic farms, urban waste diversion programs, and biosolids production suggest that organic N materials are more available than ever. Understanding where and at what rate they are applied is, however, speculative for the most part.

♦ See also solid waste (Table 14) and manure (Table 15)



Table 13. Agronomic nitrogen use efficiency of crops

Indicator Data sources and approach N use efficiency (Section 3.2.3; Table 3.1; Appendix 3.1)

Compilation of peer reviewed literature (Table 3.1)

Uncertainty and information status ♦ Measures of agronomic nutrient use efficiency (NUE) are ratios of plant nutrient uptake to nutrients

applied. Sophisticated methods have been developed, including using isotopes, to precisely determine how much of the applied nitrogen (N) gets into the plant. However, methods that are able to differentiate between the sources of N have to be performed under controlled conditions in a research plot. How closely these values reflect NUE in field conditions is uncertain.

♦ Comparatively few controlled experiments of NUE have been performed on the breadth of California crops under irrigated Mediterranean conditions (Table 3.1).

• Historically, NUE has been the focus of agronomists and there are good data measuring NUE in cereals, especially rice, dating back into the 1960s.

• Although there have been a significant number of N rate trials on other crops, often the methods are inadequate to determine fertilizer NUE. For example, some trials for tree crops do not include a zero-N plot. Without a zero-N plot to compare yields and N harvested, it is impossible to differentiate between yield derived from N mineralized from the soil or fertilizer N applied.

♦ There is uncertainty about the carryover and availability of N not assimilated from one season to another. A fraction of fertilizer N applied is sequestered by the soil and may become plant available in subsequent seasons. However, studies using isotopic methods capable of measuring uptake over multiple seasons are rare. In high rainfall areas, the excess N may be leached. But in low-rainfall areas with efficient irrigation and deep-rooted trees, the fate of N is more uncertain.



Table 14. Solid waste

Indicator Data sources and approach Landfill surveys (Section 4.1.4; Table 4.10; Appendix 4.2.7)

CalRecycle conducts surveys of materials disposed of in landfills every three years. Nitrogen (N) tonnage was calculated based on the typical N content of organic materials (see Table 4.10). There are no data sources for the disposal of synthetic N in landfills.

Biosolids (Section 4.1.5; Appendix 4.2.7)

CalRecycle reports the tonnage of biosolids based on the estimates of the California Association of Sanitation Agencies. The N content was based on Metcalf and Eddy (2003).

Uncertainty and information status ♦ There is limited evidence on the ultimate fate of products containing synthetic N. Both nylon and

acrylonitrile butadiene styrene (ABS) plastic are recyclable, but the rate of recycling is unknown. Recycling rates are likely higher in particular sectors, like automobiles and electronic waste. Some products, like polyurethane, are incorporated with other materials and are difficult to recycle. The lifespan of most synthetic N products is years to decades, with the non-recycled waste disposed of in landfills.

♦ The wet tonnage of green waste disposed in landfills is reported by CalRecycle based on surveys of arriving materials.

• There is medium evidence for the dry tonnage of N in green waste. • Food waste has been well characterized. Food waste can occur in many ways, but there is no

standard definition or accounting system other than measuring what arrives in the landfill. Thus, food waste is a mixture of inedible materials (e.g., watermelon rinds, bones. etc.), processing waste, spoilage or food in retail settings past its due date, and uneaten food.

• Yard waste is composed of materials like grass clippings, senesced leaves and wood which differ by more than an order of magnitude in N content. Because there is not a breakdown of yard waste into its various constituents, it is difficult to assign a typical N content.

• There is limited evidence for the N content of wood products other than lumber. While derived from wood, there is limited evidence of whether N is removed during the manufacturing process for these products.

♦ It is tentatively agreed by most that a growing number of waste agencies are diverting green waste, including both yard waste and food waste, from landfills to composting operations in California. However, the magnitude of N diverted and the fate of this compost is suggested but unproven. In addition, there may be some issues with the quality of the ultimate product as there can be other materials (e.g., plastics) or chemicals mixed in during the collection of the organic materials.

♦ The tonnage of biosolids is tentatively agreed by most. The statewide tonnage is reported by CalRecycle based on data from the California Association of Sanitation Agencies. However, the reported N content can vary widely. The fate of these materials is also tracked and the vast majority is land applied with alternative daily cover at landfills a secondary use. While these materials are tested for nine regulated pollutants and pathogens, there is some concern about using biosolids as a nutrient



amendment on cropland, especially food crops. ♦ It is agreed but incompletely documented that emissions from landfills is an insignificant flow of N.

Landfills are permitted to be lined such that leaching is minimized. However, landfill liners degrade over time and may leak N-rich effluent into groundwater. Because liner leaks form thin plumes of NO3

-, they are often difficult to detect with monitoring equipment. It is well established that emissions of N2O are fairly insignificant. However, the magnitude will depend on the type of cover used to control nuisance. Therefore, the most likely fates of N in a landfill should be storage or denitrification to N2. There is limited evidence for N2 emissions, although in their compilation of Air Pollution Emission Factors (AP42), the EPA suggests as much as 5% of landfill gas is N2. Based on the reported amount of landfill gas in the California Air Resources Board Greenhouse Gas Emission Inventory, landfill N2 emissions would be over 200 Gg N yr-1 at 5% N2 emissions, significantly higher than our estimate of landfill storage.



Table 15. Manure

Indicator Data sources and approach Manure production (Sections 3.2.2, 4.1.3; Appendix 4.2.6)

Manure production can be estimated directly based on standard values from the American Society of Agricultural Engineers (ASAE) or as the difference between estimated feed demand from ASAE and the measured production of animal products (see Table 4.7).

Manure composting (Section 3.2.2)

The CalRecycle Solid Waste Information System provides information on the location and types of materials used at composting facilities, but not information on tonnages of materials actually composted.

Gaseous emissions (Sections 4.1.3, 4.1.7; Appendix 4.2.6)

Livestock NH3 emissions were based on livestock populations reported in the USDA Agricultural Census and emission rates (EPA, 2004).

Fertilization with manure (Sections 3.2.2, 4.1.2)

There are no data sources for the actual use of manure as fertilizer.

Uncertainty and information status ♦ Because of new regulations taking effect in the Central Valley, every regulated dairy will be

completing a nutrient management plan (NMP) which aims to track nitrogen (N) across the entire dairy operation. While it is still not clear in what form these data will be publically available, in theory, the NMPs will be an extremely valuable source of information about N. While all flows of N will be quantified, some will be more reliable than others.

♦ Manure N production rates depend on the breed of cattle and the efficiency of milk production. • There is no comprehensive population numbers by breed. However, in the Central Valley, the

predominant breed is reported as part of nutrient management plans to comply with the 2007 Waste Discharge Requirements General Order for Existing Milk Cow Dairies.

• Using the standard values from the American Society of Agricultural Engineers, the efficiency of dairy production is ~25%. However, if the efficiency were 30%, there would be 20 Gg N more in milk and less manure N produced annually with the same feed intake.

♦ The livestock NH3 emissions are based on livestock populations and livestock or facility-type specific volatilization rates. While there is high evidence for the populations, there is limited evidence for the NH3 emissions from the various types of animals and types of manure management at facilities. While the EPA (2004) methodology attempts to estimate emission rates for the various types of manure management systems, there is limited data on the number of each type of facility in California.

♦ The extent of organic N use as a primary fertilizing material or as a soil amendment is largely speculative. Sales of organic N products are not tracked and data on distribution and rates of use for all organic N sources are largely unavailable.



• Potential manure production is calculated based on dairy herd sizes for several counties as part of their annual Agricultural Commissioners’ report. The ultimate fate of this dairy manure is unknown, but it is likely composted to some degree either before or after sale and used as an organic amendment.

• Manure from beef feedlots (Harris Ranch and Imperial County) is very likely composted and sold as fertilizer. Composting facilities require permits from CalRecycle and the facility locations and maximum tonnages of materials are available in the Solid Waste Information System. However, the actual amounts and types of materials passing through these facilities are not tracked, nor is the destination or use of the composted product tracked.

• Manure applications on cropland, based on regulations of the Central Valley Regional Water Quality Control Board, are limited to between 1.4 and 1.65 times crop uptake. The amount of manure applied is collected by each dairy for regulatory compliance, but the data are not currently available to the public.

♦ Estimating fertilization rates is complicated by the large amount of manure produced in the state. More likely than not, manure N replaces synthetic fertilizer as the source of nutrients for many of the acres of forage crops near dairies. However, it is not clear if this replacement is complete or if these crops still receive some inorganic fertilizer. Further, a large fraction of solid manure appears to be composted to some degree and applied as an organic amendment to soils. The N embodied in the manure may or may not be accounted for as part of nutrient management plans.



Table 16. Groundwater nitrogen

Indicator Data sources and approach Leaching fraction and leaching amount (Section 4.1.2; Appendix 4.2.10)

Compiled public data that reported either the fraction of fertilizer applied, or the amount of N leached in irrigated California cropland soils (see Appendix 4.2.10).

Denitrification (Section 4.1.9; Box 4.4; Appendix 4.2.10)

Denitrification was estimated with three approaches: (1) assuming a half-life of nitrate of 80 years (Landon et al., 2011) and historical estimates of nitrogen (N) inputs to groundwater, (2) multiplying estimates of the volume of groundwater recharge (CA DWR, 2003) by a fixed denitrification rate of 0.04 mg N L-1 yr-1 (Landon et al., 2011), and (3) assuming a fixed fraction of N inputs to groundwater is denitrified (Seitzinger et al., 2006; Leip et al., 2011).

Uncertainty and information status ♦ Leaching of N occurs when excess water transports excess N below the rooting zone. The mass

balance calculated the load of N in leaching in two ways: (1) multiplying measured N concentrations in the leachate by the modeled volume of recharge volume, and (2) calculating the mean of the percentage of applied fertilizer that was reported as leaching. These two metrics differed by 120 Gg yr-

1, which is one of the largest sources of uncertainty in the mass balance. Assuming only a small soil storage term (5 Gg N yr-1) from repeated sampling of cropland soils 55 years apart and calculating leaching by difference would suggest that the higher estimate calculated with the first method is the better estimate.

♦ There is limited evidence for the fraction of fertilizer that leaches and the N concentration in leachate, but these measurements are typically made completely independent of each other. Both of these measurements vary in time and space based on the crop type, soil characteristics, and agronomic practices. In many mass balances, the leaching term is calculated by difference because of the difficulty in measuring it.

♦ The sampling of groundwater wells for NO3- is not a random sample of groundwater basins which

could bias the estimates of the total amount of N in groundwater. If the samples were more likely to be taken in areas with NO3

- contamination or at shallower depths, the estimate of the median groundwater concentration would be higher than the actual median.

♦ While there were multiple approaches to estimating groundwater denitrification, there are relatively few measurements from California to choose which approach is preferable. Therefore, the estimate of this flow is tentatively agreed by most.

♦ Drainage can affect the fate of N leaching through soils. The presence of drainage can divert recharge to surface water instead of groundwater, leading to an overestimate of the N flow to groundwater. However, there is low agreement on how to define and survey drainage.

♦ The most recent national estimate of drained land suggests that over 1 million ha are present in California (Pavelis, 1987); however, it is unclear what the exact definition of “drainage” is. The acreage with subsurface drainage is likely considerably lower than this estimate, largely concentrated



in Imperial County and in the west side of the San Joaquin Valley. While there is no centralized database mapping the location of drained lands, it is likely that tile drained lands predominantly occur in these closed basins with no drainage to the ocean. With the exception of the Grasslands Bypass project, it is very unlikely that there are any other major sources of NO3

- to rivers from subsurface tile drainage in California.

♦ It is very likely that surface agricultural drains are a major source of NO3- to rivers in California as

some of the highest concentrations of NO3- (reported in Kratzer et al., 2011, Sobota et al., 2009) were

observed in small creeks that are dominated by irrigation return flows during the summer (e.g., Orestimba Creek).

♦ We assumed no transport of N between surface water and groundwater. While it is well established that water flows bi-directionally between rivers and groundwater in California, there is limited evidence for the magnitude of N transport. It is also well established that the hyporheic zone, where groundwater and surface water meet, is a particularly active location for N transformations such as denitrification.

♦ See also Nitrous oxide (N2O) and dinitrogen (N2) gas (Table 5)



Table 17. Surface water nitrogen

Indicator Data sources and approach River discharge N (Section 4.1.8; Appendix 4.2.9)

For large rivers in California, nitrogen (N) concentrations and flow data are monitored by the US Geological Survey (USGS). While the flow data are typically measured hourly, the N concentrations are measured weekly to monthly. Thus, the N load is calculated by modeling (e.g., LOADEST) to be able to infer the values in between sampling points. For California, published sources with river N discharge data include Sobota et al., 2009, Schaefer et al., 2009, and Kratzer et al., 2011. N discharge in smaller watersheds is monitored sporadically around the state, but two potential sources of data are the Surface Water Ambient Monitoring Program of the State Water Resources Control Board and the California Water Quality Monitoring Collaboration Network.

N retention (Section 4.1.8; Appendix 4.2.9)

Rivers and lakes have the ability to retain N. That is, N flowing in from rivers does not flow back out. Retention can be estimated by mass balance (i.e. the difference in N inputs to rivers and the N load carried to the ocean). Retention can be partitioned into gas losses and burial in sediment based on global estimates of denitrification (Seitzinger et al., 2006).

Uncertainty and information status ♦ The N load in major rivers in California is well established.

• Bias in the LOADEST model used to calculate annual N loading from periodic water samples is a minor source of uncertainty. In general, the model tends to overestimate at low flows and underestimate at high flows.

• A larger source of uncertainty is inter-annual variability. Unlike agricultural production and fossil fuel combustion which vary by less than 25% from year to year, river N loads fluctuate dramatically; largely related to variability in precipitation. Based on the data for 1975-2004 in Kratzer et al. (2011), there is a five-fold difference in the Sacramento and Santa Ana Rivers and a 10-fold difference in the San Joaquin River between the highest and lowest annual loading value.

♦ While the major rivers in California are monitored by the USGS, many of the small coastal watersheds are not. There is no centralized database for water quality monitoring by other agencies or non-governmental organizations. The Surface Water Ambient Monitoring Program at the State Water Resources Control Board and the California Water Quality Monitoring Collaboration Network are potential sources of data, but were not used in the mass balance calculations.

♦ There is high potential for N storage in California reservoirs, however information is limited and differences in calculations make broad generalizations difficult. While the buildup of sediment in reservoirs is well established, the fate of N in reservoirs is speculative.

• The most common approach to examining the effects of reservoirs on N is to calculate N retention as riverine N inputs minus dissolved N outputs. Thus, both sedimentation and denitrification contribute to N retention.

• Harrison et al (2008) reported N retention an order of magnitude higher in reservoirs



compared to lakes. • There is high potential for denitrification in reservoirs, but there are relatively few empirical

studies separating N retention between sediment burial and denitrification. The most thorough review of denitrification (Seitzinger et al. 2006) lumps lakes and reservoirs together. However, if N retention is 10 times higher in reservoirs, it is not clear if denitrification would be similarly higher.



Table 18. Dissolved nitrogen in waste discharge

Indicator Data sources and approach Nitrogen discharge (Sections 3.6, 4.1.5; Appendix 4.2.7)

The flow of effluent and nitrogen (N) concentration are monitored by most regulated dischargers in the state. The data are available from the Regional Water Quality Control Boards, but often only in hard copy at regional offices. For dischargers to surface water the United States EPA should also receive the same data, but access to this information is unclear.

Uncertainty and information status ♦ In California, there are approximately 900 regulated wastewater treatment facilities that discharge to

land or surface water. Availability of monitoring data varies and is dependent on the permitting agency.

• In practice, the Regional Water Quality Control Boards issue permits regardless of the location of discharge, even though discharge to surface waters is regulated by the Clean Water Act under the authority of the United States EPA.

• Surface water permits (i.e. National Pollutant Discharge Elimination System (NPDES)) are renewed every five years and typically have more extensive monitoring compared to the land discharge permits (Waste Discharge Requirement NON15) subject to the California Water Code.

• Almost 300 facilities have permits to discharge to surface water (including the Pacific Ocean), but this includes 44 of the 50 largest facilities in the state. As there are typically more stringent regulatory and monitoring requirements for facilities that discharge to surface water, there is more evidence for the N discharge from large facilities compared to small facilities. However, data suggest (see Figure A4.2.1 in Appendix 4.2.7) that there is a strong relationship between size of facility and N discharge across the five order of magnitude range in facility size.

• Currently, only Region 2 (San Francisco Bay) has an electronic database that provides easy access to monitoring data for wastewater treatment plants, although most other regions are aiming for this in the near future.

♦ N concentrations in wastewater discharge are well established with ranges of values published in wastewater engineering textbooks.

• The frequency of monitoring (monthly) for most facilities is largely sufficient for estimating N loads. San Francisco is the only large city that has mixed stormwater and sewage treatment which results in dramatically different flows in winter and summer.

• Most facilities without N removal treatment only measure ammonia, resulting in limited evidence for the concentration of other forms of N. Based on the available data, nitrate and organic N contribute 10% - 20% of the total N discharge.

• Facilities with N treatment typically only measure NO3-. There is limited evidence for the

amount of dissolved N that is actually removed. While the technology exists to remove up to ~80% of the N, in many cases facilities are only converting NH3 to NO3

- without converting significant quantities of N to inert N2 gas. On average, the NO3

- concentrations at facilities in California with N treatment were 66% less than the NH3 concentration at facilities without N treatment, but there was high variability among facilities.



♦ Food processors also discharge N, predominantly as organic N. Like wastewater treatment plants, these facilities are considered point sources and can be authorized to discharge to surface water with a NPDES permit, to land with a NON15 permit, or through industrial stormwater permits. Unlike wastewater treatment plants where there are predictable N concentration values and discharge, food processing facilities vary by orders of magnitude in N concentrations and can be highly seasonal in the magnitude of discharge. The monitoring data are less accessible for food processing plants, but the limited evidence suggests that some large processors annually discharge as much organic N as a city of 100,000 people.



Table 20. Nitrogen in drinking water

Indicator Data caveats and sources of uncertainty

Historic trends in groundwater nitrate (Section 5.2.1.5)

Despite reporting significant increases in groundwater nitrate over time, Boyle et al. (2012) note that it is difficult to establish accurate historic trends because the data used in their analysis contained a relatively small number of samples prior to 1990 which was comprised mostly of public supply wells, and a large increase in the number of samples from domestic and irrigation wells at dairies beginning in 2007. Since public supply wells tend to be deeper and have somewhat lower nitrate concentrations than the shallow wells used for domestic and agricultural purposes, this change in data sources would tend to exaggerate the increasing trend particularly after 2007 (Figure 5.2.5). Acknowledging this caveat, the overall trend was still increasing throughout the study area prior to 2007.

Future projections of groundwater nitrate (Section 5.2.1.5)

The nitrate transport model projections reported by Boyle et al. (2012) suggest that groundwater nitrate concentrations will increase in the future, but the data exhibit significant spatial and temporal uncertainty. This uncertainty is due to inherent variability in nitrogen (N) loading (i.e. N losses to the environment) across different land use and source types (e.g., agricultural crops, septic systems, manure lagoons). But while the model may not forecast future groundwater nitrate levels with a high degree of accuracy, it is still seen as a useful tool for evaluating the impact of alternative land use management scenarios that might be adopted in the future.

Proportion of wells with elevated nitrate (Section 5.2.2.2)

Data compiled from the California State Water Resources Control Board suggest that 9.8% of approximately 16,000 wells tested had at least one value greater than the drinking water maximum contaminant levels (MCL = 10 mg nitrate-N L-1), and 5.8% had median levels greater than this value (Figure 5.2.5). The proportion of wells with average and maximum values exceeding the MCL and median values >3 mg nitrate-N L-1 also varied across the state. These results should be thought of as general indicators, as the wells in this database included many types of wells, some of which were drilled specifically to characterize areas thought to have high nitrate levels.

Exposure to groundwater nitrate (Section 5.2.2.2)

While efforts are underway to compile service maps and information on the number of people served by water systems of various sizes (e.g., self-supplied, public and community water systems, etc.), at present such data are very limited for many counties in the state (C. Wolff, personal communication). As such, accurate estimates of the number of people in each county and statewide that are at risk of exposure to high nitrate drinking water are difficult to make at present.



Table 21. Nitrous oxide emissions from agricultural soils

Indicator Data sources and approach IPCC Default Emission Factors (EFs) for direct and indirect N2O emissions

(Section 5.4.3)

Reported by IPCC (2006) as part of their guidelines for Tier 1 inventory methods.

Uncertainty and information status ♦ The IPCC’s default EFs for direct and indirect N2O emissions are derived from a large number of

field experiments (>100 studies) conducted globally in recent decades. Considerable variation in experimental N2O flux measurements averaged across many environmental conditions, geographic and temporal scales, and cropping systems introduces a high degree of uncertainty in the IPCC default EFs for N2O. For example, the uncertainty in direct N2O emissions from agricultural soils ranges from 0.003 to 0.03 kg N2O – N per kg N applied. Despite this caveat, emissions calculations based on default EFs remains a useful accounting method for national and regional greenhouse gas



Table 22. Nitrogen-related air pollutants

Indicator Data sources and approach Historic trends in NOx, O3 and PM2.5

(Section 5.3.2 and Section 5.3.3)

Reported by Parrish et al. 2011 using data from monitoring stations in the South Coast Air Basin.

Spatial trends in NH3

(Section 5.3.2)

Reported by Clarisse et al. (2010, 2009) using satellite data retrieved from an infrared atmospheric sounding interferometer (IASI).

Spatial trends in PM2.5

(Section 5.3.2)

Reported by van Donkelaar et al., 2010 who compared surface measurements of PM2.5 with satellite data retrieved from a moderate resolution imaging spectroradiometer (MODIS) and multi-angle imaging spectroradiometer (MISR).

Effects of O3 on crop yields

(Section 5.3.6.2)

Reported by Grantz and Shrestha (2005) using data from O3 dose response models.

Uncertainty and information status ♦ Historic trends in these air pollutants in California are based on data originally reported by Alexis et

al., 1999 and Cox et al., 2009. The data for each pollutant are available from the CARB at http://www.arb.ca.gov/aqd/almanac/almanac99/appendix.htm. While the overall decline in criteria air pollutant levels is relatively certain, the rate of decline varies considerably among regions. This spatial variability is influenced by a wide range of factors including emissions sources, economic activities, seasonal and climatic differences, and the relative quantity of each primary

♦ Satellite data on spatial trends in NH3 were drawn from a dataset with global geographic coverage. The mapping procedure used by van Donkelaar et al., 2006 is based on the linear relationship between the brightness temperature difference and the retrieved total NH3 column. Their regression analysis on a wide selection of regions at different times of the year showed that 1 K correspond to 15± 7.5 mg m-2 of NH3 with a confidence interval of 80%.

♦ A comparison of measured and satellite-estimated PM2.5 indicated a statistically significant relationship with an uncertainty of ± (1 μg/m3 or 15%).

♦ Uncertainty in yield losses by crops due to O3 can result from variability in both the level of O3

exposure and the sensitivity of the crop.



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CHAPTER FOUR

A California Nitrogen Mass Balance for 2005

Appendix 4.2 Mass balance methods and data sources

Lead Authors:

D. LIPTZIN AND R. DAHLGREN

Contributing Author:

T. HARTER



Suggested citation:

D Liptzin, RA Dahlgren, and T Harter. “Appendix 4.2: Mass balance methods and data sources for Chapter 4: A California Nitrogen Mass Balance for 2005.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 4.2 MASS BALANCE METHODS AND DATA SOURCES 2

4.2 Mass Balance Calculations and Data Sources

The imports of new reactive nitrogen (N) for the statewide mass balance were fossil fuel combustion, biological N fixation, synthetic N fixation, agricultural feed, and fiber. The exports were gas/particle exports in the atmosphere, food exports, discharge of rivers to the ocean, and discharge of sewage to the ocean. Storage terms include soils and vegetation, reservoirs, landfills, and groundwater. We assumed no storage in the atmosphere. In addition to the calculations at the statewide level, mass balances were calculated for various subsystems within California: natural land, cropland, urban land, livestock, households, surface waters, groundwater, and the atmosphere. In most cases, the flows in the subsystems could be estimated with one or more independent approaches, but some flows could only be estimated by differences (e.g., groundwater in cropland).

For the calculations of flows in the three land-based subsystems, California was classified into four main land cover classes: natural land, cropland, urban land, and water. An updated version of the California Augmented Multisource Landcover (CAML) map was produced by the Information Center for the Environment at the University of California – Davis (ICE 2006). The base map layer of CAML was the 2002 Multi-Source Land Cover dataset produced by the California Department of Forestry and Fire Protection (FRAP). This layer was the source for the type of ecosystem vegetation in all of the natural land and also delineated surface waters. For biome level estimates, the FRAP vegetation types were lumped into biomes based on the California WHR13 classes: barren, desert (desert shrub and desert woodland), forest (hardwood and conifer), herbaceous, shrub, woodland (hardwood and conifer), water, and wetland. The agricultural land was further subdivided into individual crops based on the class and subclass of the polygons in the most current digitized county maps produced by the California Department of Water Resources (DWR). For counties without digitized DWR maps, agricultural land was identified based on the categories in the FRAP base layer, supplemented with crop information from pesticide use reports produced by the California Department of Pesticide Regulation. Urban areas were identified by combining the urban boundaries indicated in the California Department of Conservation Farmland Mapping Program and urban land-use types in the 2001 USDA National Land Cover Dataset. The water pixels in CAML were divided into lakes, reservoirs, and rivers in two ways: (1) areas identified as riverine and estuarine wildlife habitats were categorized as rivers, while lacustrine wildlife habitats were categorized as lakes, and (2) in pixels identified only as water, the spatial location of the pixel was compared to the USGS National Hydrography Dataset (USDI 2013); if the pixel matched a lake or reservoir, the pixel



was designated a lake or reservoir, otherwise the water pixel was considered a river. The final map was produced at a 50 m resolution.

4.2.1 Fossil Fuel Combustion

Fossil fuel combustion produces NOx, NH3, and N2O as incidental by-products which are tracked and regulated for different reasons. Nitrogen oxides are considered a criteria pollutant and all of the anthropogenic sources of NOx included in the statewide inventory conducted by the California Air Resources Board (ARB) and the US EPA were considered emissions. The emissions from the 2002 EPA inventory (EPA 2007) were used for the calculations because that dataset was the basis for the N deposition model described below. Ammonia is an unregulated pollutant, but it has become part of the criteria pollutant monitoring program because of its role in forming secondary fine particulate matter (PM2.5) in the atmosphere as either ammonium nitrate or ammonium sulfate. As with NOx, the 2002 EPA dataset was used to estimate NH3 emissions; however, only categories related to fossil fuel combustion (fuel combustion, highway vehicles, and off-highway vehicles) were included. Finally, N2O emissions are not yet regulated, but are estimated as part of greenhouse gas inventories by both ARB and the EPA. All “included” fossil fuel combustion sources from the ARB inventory, regardless of sector, were used to calculate fossil fuel related N2O emissions and an average for 2002-2007 was calculated.

While not necessarily exclusively from fossil fuel combustion, there is import of reactive N to the atmosphere above California from outside the boundaries of the study area. Some of this N will be transmitted completely through the state and this fraction will be ignored. However, we estimated the import of this reactive N by assuming that the offshore N deposition rate would occur across the entire state of California in the absence of any emissions from California. Based on the atmospheric deposition rates generated by the Community Multiscale Air Quality (CMAQ) model in areas off the coast of California as modeled by Tonnesen et al. (2007), the current offshore deposition rate is 1 kg N ha-1 yr-1, split evenly between oxidized and reduced N.

4.2.2 Atmospheric Deposition

Atmospheric deposition was based on the results of Fenn et al. (2010). Their Geographic Information System (GIS) map layer uses output from the CMAQ model based on 2002 emissions data. The CMAQ model results for most of the state were available from Tonnesen et al. (2007) at a resolution of 4 km, but for northern and southeastern California only the 36 km CMAQ output from the EPA was used to create a statewide map. In certain biomes, based on the



availability of field measurements, the model output was replaced by measured deposition data.

Total N deposition was partitioned statewide on the various land-use types (natural land, cropland, urban land) based on the land-cover map. However, as the composite statewide map in Fenn et al. (2010) only provided total N, the ratios of oxidized to reduced and wet to dry N deposition were calculated based on the area modeled by Tonnesen et al. (2007).

We assumed that storage was not possible in the atmosphere. Therefore, the export of NOx and NH3 was calculated as the difference between all inputs and N deposition. By the time the export from California occurs, secondary reactions will have occurred in the atmosphere such that NOy (NOx plus its oxidization products like HNO3 or organic nitrates) and NHx (NH3 plus the NH4

+) better describe the forms of N. We assumed that all of the emitted N2 and N2O were exported from the study area.

4.2.3 Biological N Fixation

Biological N fixation is also discussed in Chapter 3. A variety of field measurements of biological N fixation have been used including 15N isotope methods, acetylene reduction, N accretion, and N difference, which vary in their assumptions and limitations.

4.2.3.1 Natural Land N Fixation

Based on the USDA Plants database (USDA 2013), a total of 56 native and 34 non-native, non-crop species are known to be symbiotic N fixers on natural land in California. However, field measurements of rates and the relative abundances for most of these species are poorly known.

Therefore, we used three approaches, based on Cleveland et al. (1999), to estimate biological N fixation in natural land. First, the biome areas calculated from the land-use map were multiplied by the biome-specific N fixation rates compiled in this global synthesis of published rates. A range in values was estimated using the biome-specific low, medium, and high percent cover abundance of the N fixing species. Second, Cleveland et al. (1999) developed an empirical linear relationship between biome-specific modeled values of actual evapotranspiration (ET) and N fixation rates. The mean modeled statewide ET (provided by Q. Mu, University of Montana) from 2001 (33.6 cm yr-1) was used because it was the only year when precipitation, modeled ET, and cropland irrigation rates were available for the entire state. Third, we used a mass balance approach. That is, we estimated all of the other N flows in and out of natural land, assumed steady-state conditions (i.e. no change in N storage) and calculated N fixation by difference.



4.2.3.2 Cropland N Fixation

Cropland N fixation rates were based on published species specific rates and harvested acreages.

The most comprehensive analysis of legume N fixation rates is a meta-analysis for Australia described in Unkovich et al. (2010). These authors found highly variable rates, but a strong positive relationship, between fixed N in aboveground tissues and productivity. This may help explain, in part, the high variability in the published rates. The only crop included in this analysis that is grown on a significant acreage in California was alfalfa where the empirical relationship was aboveground fixed N (kg ha-1) = 18.2*Production (Mg ha-1) + 0.13. The rates for the other leguminous crops grown in California (dry beans, dry and fresh lima beans, snap beans, and clover), but not included in the analysis, were based on Smil (1999). We also include the fixation rates for rice paddies reported by Smil (1999) associated with the cyanobacteria symbiotically associated with aquatic ferns in the genus Azolla. Crop acreages for all legumes except clover were calculated as the 2002-2007 average of the annual harvested acreages reported in the statewide database of California Agricultural Commissioners’ reports (USDA NASS 2013).

Clover used to be planted widely in irrigated pastures, but now is estimated to compose only 10% of the cover in these systems (M. George, personal communication). The acreage of irrigated pasture was calculated as the average of the 2002 and 2007 Agricultural Census acreage for irrigated pasture (see Table 10 in USDA 2004 and 2009)

4.2.4 Synthetic N Fixation

Synthetic N fixation is largely the result of the Haber-Bosch process, although a small amount of ammonium sulfate is still produced as a by-product from coke oven gas during steelmaking (Kramer 2004). This industrial process converts atmospheric N2 to NH3 at high temperature and pressure with natural gas being the source of hydrogen and energy. National estimates of fixed N are annually compiled by the United States Geological Survey including national production, imports and exports. Fixed NH3 is the feedstock for essentially all synthetic N fertilizers as well as a variety of industrial N-containing chemicals and explosives (Kramer 2004). Less than 2% of the national explosives use occurs in California because of the limited amount of mining (USDI 2000). Ammonium nitrate/fuel oil mixtures are the dominant form of explosives, but we assumed that the N emissions from their use was N2 gas. Therefore explosives were not considered as part of the budget.



4.2.4.1 Non-Fertilizer Synthetic Chemicals

Non-fertilizer use of some individual compounds can be tracked, but as a whole it is typically calculated as the difference between total NH3 fixation and fertilizer use. Other common non-fertilizer uses include synthetic chemicals, such as melamine, nylon, plastics (e.g., acrylonitrile butadiene styrene), and polyurethane (Table A4.2.1). Several estimates of synthetic N consumption are available, but the Kramer (2004) estimate was used because it breaks down the non-fertilizer N consumption most completely (Table A4.2.2). The national total for non-fertilizer consumption of N was 1,722 Gg N yr-1 (Kramer 2004). Excluding synthetic N for explosives, 567 Gg N yr-1 of non-fertilizer N was consumed nationally in 2002 (Kramer 2004).

We scaled the national estimate to California based on the mean 2002-2007 population of California (35.6 million) and the United States (295 million) from the United States Census Bureau (2013). We used the United States Census as opposed to the California Department of Finance population estimate in order to make the most consistent estimate of California’s proportion of the United States population. Most of these synthetic forms of N are assumed to be long-lasting chemicals, which become part of infrastructure and household items and eventually are disposed of in landfills (Table A4.2.1). One chemical class that is poorly tracked is N-containing compounds found in many common household products, such as surfactants and detergents that end up as part of the wastewater stream. TABLE A4.2.1. Major Non-Fertilizer Uses of Synthetic Nitrogen in the United States. Source: Domene and Ayres 2001. Compound N (Gg yr-1) End use Acrylonitrile 173 Acrylonitrile Butadiene Styrene Caprolactam 86 Nylon Hexamethylenediamine 203 Nylon Isocyanates 90 Polyurethane Melamine 54 Laminates and surface coatings Urea 180 Resins Adipic Acid1 185 Nylon Manufacturing Methyl methacrylate2 102 Acrylic glass manufacturing 1NOx, N2O, and N2 emissions from the reduction of nitric acid are a byproduct of adipic acid synthesis, but nitrogen is not a component of the product. 2Ammonium sulfate, typically used as fertilizer, is produced as a by-product of methyl methacrylate synthesis.



TABLE A4.2.2. Synthetic Nitrogen Consumption (Gg N yr-1) in the United States. Where possible, non-fertilizer consumption was partitioned into explosives, plastics and synthetics, and other uses. Source Year Fertilizer Non-

fertilizer Explosives Plastics and

synthetics Other

Kramer 2004 2002 11,636 1,565 998 491 76 UN FAO 2010 2002 10,945 4,277 Domene and Ayres 2001 1996 11,297 3,020 557 786 1677

4.2.4.2 Synthetic Fertilizer

Fertilizer sales, not necessarily fertilizer use, have been reported annually since the 1950s in the tonnage reports of the California Department of Food and Agriculture. These data are identical to the California data compiled by The Fertilizer Institute as part of their national survey. To prevent duplication, reporting of sales is supposed to occur when a licensed fertilizer dealer sells fertilizer to an unlicensed purchaser. The data are collected as tonnage of fertilizing materials and are converted to tons of nutrients based on the reported fertilizer grade. Fertilizer use was assumed to be on average equivalent to fertilizer sales at the state level. Because of uncertainty in these data starting in 2002, we used the average synthetic fertilizer sales for 1997-2001.

Synthetic fertilizer use was first partitioned between agricultural and urban (i.e. turfgrass) use based on data provided by the Scotts Miracle-Gro Company. Annually, an estimated 2.7 million tons of fertilizer is applied nationally to turfgrass. It is divided equally between homeowner use, commercial application to home lawns, and golf courses/athletic fields. This fertilizer tonnage was converted to N tonnage based on the typical N grade of lawn fertilizer (29%) based on the popular Scotts Turf Builder product. The national estimate was scaled down to California using remote-sensing based estimates of turfgrass acreage. California contains 11,159 km2 of turfgrass, or 6.8% of the total national turfgrass acreage (Milesi et al. 2005). The Scotts Company was willing to share their sales figures for the state and reported sales of 4 Mg N sold in 2005 for the do-it-yourself homeowners market. Their research suggests that they supply approximately half of the do-it-yourself homeowners market.

Synthetic fertilizer use for cropland was calculated separately for ornamental horticulture and other crops. The amount used for environmental horticulture was based on the acreage of open grown commodities in the USDA Census of Agriculture, an annual irrigation rate of 2 m water yr-1, and a N concentration of 100 ppm N assuming no recycling of N in irrigation water



(R. Evans, personal communication). Sod farms were assumed to use 400 kg N ha-1 (R. Green, personal communication).

Synthetic fertilizer use on other crops was calculated by subtracting urban and environmental horticulture use from the total sales. Fertilizer use can also be validated based on crop-specific recommendations. Current (since 1999) fertilization rates by crop were extracted from UC Davis cost studies and the USDA Chemical Use Surveys and the two data sources were averaged (see Chapter 3 for further details on data). The fertilization rates were combined with the crop-specific acreages reported in the statewide Agricultural Commissioners dataset to calculate a total fertilizer recommendation that could be met with synthetic fertilizer or manure. Any difference between the calculated fertilizer use and the synthetic fertilizer sales for these crops indicates fertilization needs met by manure.

4.2.5 Agricultural Production and Consumption: Food, Feed, and Fiber

The production and consumption of food, feed, and fiber involve the majority of N flows in California. The N tonnage of all agricultural products, with the exception of wood products and ornamental horticulture, was calculated from production data compiled by the county Agricultural Commissioners (USDA NASS 2013). The 253 crop commodities in the database were consolidated into 121 classes based on similar characteristics. The 2002-2007 average N tonnage was calculated by matching each crop class to the most similar crop in the USDA Crop Nutrient Tool (USDA 2013). This database, which is the most comprehensive source of its kind, is a compilation of the nutritional content of crops from a variety of published sources, but most of the sources are at least several decades old. The only commodity not present in the database was olives whose nutritional information was based on the 2009 USDA National Nutrient Database for Standard Reference (USDA ARS, n.d.). Commodity boards in the state were contacted to determine if they had more recent and California-specific data, but only the Almond Board of California provided information. The following crop classes were considered feed crops: alfalfa hay, almond hulls, grain and silage corn, cottonseed, non-alfalfa haylage, small grain hay, grain and silage sorghum, tame hay, and wild hay.

Consumption of agricultural products was based on the population of humans, household pets, and livestock in the state. The average population of California during the period 2002-2007 was 35.6 million. The consumption of food was calculated in two ways. First, on average from 2002-2007, the national per capita food availability was 6.5 kg N yr-1 (USDA ERS 2013). Second, per capita N consumption varies globally, but in the United States, 5.0 kg N yr-1 is typical (Boyer et al. 2002). The waste of food by retailers, food service, and consumers has been



estimated at 27%. Combining food waste with food consumption leads to a per capita demand of

6.4 kg N yr-1, almost identical to the USDA Economic Research Service (USDA ERS 2013) estimate of food availability. Thus, a per capita value of 6.4 kg N yr-1 was used to calculate human food supply. Household pet populations were determined from the American Veterinary Medical Association (AVMA) survey of pet ownership (AVMA 2007)). Total household pet food consumption was based on an average body mass of dogs and cats (Baker et al. 2001) and daily N intake requirements (NRC 2006).

Nursery and floriculture N harvest was based on annual biomass production of 750 kg N ha-1 (R. Evans, personal communication) and the average of the reported acreage from the 2002 and 2007 USDA Census of Agriculture for all open grown horticultural commodities. We assumed that there was no net export of horticultural commodities. Based on the value of sales reported in the 2009 Census of Horticultural Specialties (USDA 2010), California produced 20% of the total national horticultural specialty crops. However, of the nursery and annual bedding/garden plants (which likely contribute the most to harvested N), California only produced 14%, similar to the state’s proportion of the national population (12%).

The N tonnage of lint cotton, the only fiber commodity harvested on cropland, was calculated identically to the food crops. Annual cotton consumption for the population of California from 2002-2007 was on average 1 Mg cotton (USDA ERS 2013). The wood harvest in California in 2004 was 56 million m3 (Morgan et al. 2004). This was converted to N production based on the specific gravity (0.5 g cm-3) of Douglas fir (Pseudotsuga menziesii), and a typical wood N content (excluding bark) of 0.15% (Cowling and Merrill 1966, USDA Forest Service 1999). The consumption of wood for California was based on the national per capita estimate of 67 ft3 per year of wood products scaled to the 2002-2007 average population of 35.6 million. This volume was converted to N tonnage with the same factors as the volume of wood harvested.

Livestock feed was determined based on animal populations and dietary needs. For non-cattle livestock that are raised for meat (broilers, turkeys, pigs), the population was the average of the 2002 and 2007 USDA Agricultural Census quantity of animals sold. The feed requirements for these types of livestock were estimated on a grow-out basis (Van Horn 1998). For dairy cattle, steers, and layers the population estimates were the 2002-2007 average of the USDA National Agricultural Statistics Service annual year-end inventory. All beef cows, beef replacement heifers, and all calves were assumed to be grazed on rangelands. We assumed that all dairy cattle were on feed, as more than 95% of the dairy cows were located in the counties of the Central Valley or in the Chino Basin (USDA NASS 2012) where confinement is the typical practice. The feed requirements for dairy cows were from Chang et al. (2005) with the assumption that for one-



sixth of the year the cows were dry. The feed requirement for dairy replacement heifers was based on a 440 kg Holstein heifer (ASAE 2005). Although horses are included in the USDA Agricultural Census, this survey underestimates their population because it excludes animals that are not working animals. Instead, the horse population was based on the AVMA (2007) survey of pet populations and N intake requirements were from NRC (2007). Unlike dogs and cats, the horse population was estimated regionally: the California horse population was estimated assuming that the number of horses per household was the same across the entire Pacific region (Washington, Oregon, and California). In addition, there is anecdotal evidence that horse owners in California feed alfalfa to horses in the state because it is perceived to be higher quality feed (C. Stull, personal communication). A diet of 100% alfalfa feed with the suggested dry matter intake would provide 50% more N to horses than is needed.

Livestock-based food production (milk, eggs, meat) was based on 2002-2007 average production estimates from USDA annual surveys with the exception of broilers which were the average production from the 2002 and 2007 USDA Agricultural Census (USDA 2004, USDA 2009). The N content of various products was from NRC (2003), except that turkey N content was assumed to be the same as broilers (Table A4.2.3).

Product N content (%) TABLE A4.2.3. Assumed Nitrogen Content of Animal

Products. Source: NRC 2003.

Hogs, beef 2 Milk 0.5 Eggs 1.8 Broilers, turkeys 2.3

4.2.6 Manure Production and Disposal

Manure production was calculated based on the populations used for feed requirements and animal-specific excretion rates. For dairy cows, excretion was 169 kg N head-1 yr-1 for lactating cows and 81 kg N head-1 yr-1 for dry cows (Chang et al. 2005). It was assumed that all cows were dry for one-sixth of the year and lactating for five-sixths of the year resulting in an average manure production of 208 kg N head-1 yr-1. Dairy replacement heifers excreted 43 kg N head-1 yr-1 (ASAE 2005). Excretion rates for beef steers, pigs and poultry were based on Van Horn (1998).

Horse excretion was assumed to be equivalent to feed intake (i.e. what was consumed was excreted). As with the calculations for feed intake, we assumed that all beef cows, replacement heifers, and calves were permanently on range with insignificant N inputs and outputs.



Manure N from confined animals was either leached to groundwater from the animal facilities, emitted to the atmosphere, or applied on cropland. The leaching of manure N was based on the amount of dairy manure N produced and the fraction leached from facilities reported by van der Schans et al. (2009). One source of data for livestock NH3 emissions was the 2005 EPA NH3 emission inventory for California (EPA 2008). A second method to estimate NH3 emissions was multiplying the manure production estimates described above by animal-specific NH3 emission factors from EPA (2004). Nitrous oxide produced prior to land application of manure was based on the average for the 2002-2007 manure management subsector of the ARB greenhouse gas inventory (CARB 2013). There are few quantitative estimates of N2 emissions from the housing and production portion of dairies, but they are suggested to be small (Rotz 2004).

The predominant source of manure produced in California is confined dairies. The N content of solid and liquid excreta from dairy cattle is well established. However, the manure that is applied to cropland in solid and liquid form represents a mixture of N from urine and feces diminished in magnitude by volatilization and leaching. There are no data currently that would allow for partitioning the manure applied on and off dairies into solid and liquid form. However, if the nutrient management plans required by the Central Valley Regional Water Quality Control Board become publically available, they will be an invaluable resource for understanding N flows in the dairy-forage system. The manure from pigs, poultry, feedlot beef cattle, horses, and sold dairy manure was also assumed to be applied to cropland.

4.2.7 Household Waste Production and Disposal

Per capita N availability nationally for 2002-2006 was reported as 110 g protein day-1 or 6.4 kg N yr-1 (USDA ERS 2013). Statewide per capita N consumption (4.9 kg N yr-1) was estimated based on actual protein consumption reported for various demographic groups and the populations of these groups in the United States Census for 2003-2007 following Baker et al. (2001). The consumed N was assumed to end up as sewage N. The difference between available food (228 Gg N) and food consumption (174 Gg N) was assumed to be waste. This 54 Gg N, or 23%, in waste is close to the 27% food waste reported at the retail and consumer level (Kantor et al. 1997). Food waste has several potential fates: down the sink to wastewater, composted and applied to urban land or cropland, and disposed in landfills. While the number of communities collecting household green waste is growing, we assumed that food waste went to landfills.

The tonnage of N discharged as wastewater without advanced treatment in areas with centralized sewage was calculated directly from measurements of wastewater N effluent. A list of



facilities classified as wastewater dischargers was obtained from the State Water Resources Control Board’s (SWRCB) publically available database, the California Integrated Water Quality System (CA SWRCB 2013). This list was supplemented based on manually examining the list of dischargers without a category or those in the ‘other’ category. In addition, effluent discharge, and in many cases effluent N concentrations, was obtained. An empirical relationship was developed between design flow, which is included as part of the SWRCB facility database, and the discharge of NH3 for all of the facilities in the state that serve more than 100,000 people. Like the SWRCB, we refer to the sum of NH3 and NH4

+ in effluent as NH3. In addition, NH3 concentration and flow data were available electronically for facilities within the San Francisco Bay Regional Water Quality Board. Because the flow and N tonnage varied by more than 5 orders of magnitude, a log-log relationship was used with a polynomial fit (Figure A4.2.1). While NH3 is the only N constituent commonly measured in effluent, in a few cases, organic N and/or

NO3-

were also monitored in facilities with no N treatment and they were <10% of the total N load. A minor amount of the N loading to wastewater treatment is from sink disposals and household chemicals (e.g., Baker et al. 2007), but these are typically insignificant sources of N.

FIGURE A4.2.1. Relationship Between Wastewater Treatment Plant Design Flow and Nitrogen Discharge in California. Design flow was chosen as the predictor because it is reported by essentially all facilities to the State Water Resources Control Board. Population served is also a strong predictor of nitrogen discharge, but is not

y = -0.2113x2 + 1.8088x + 0.763R² = 0.9312

00.5

11.5

22.5

33.5

44.5

0 0.5 1 1.5 2 2.5 3

Amm

onia

Dis

char

ge (

Log

Mg

N y

r-1)

Wastewater Treatment Plant Design Flow (Log million gallons day-1)



necessarily reported as part of the Waste Discharge Requirements. The data points represent the mean value for each facility from data available for 2002-2007. The facilities chosen for this analysis included all of the large treatment plants in the state (population served >100,000) as well as all of the treatment plants in Region 2 because it is the only region with an electronic database of monitoring data.

The level of treatment in the known facilities was determined based on three data sources.

First, the orders issued by the Regional Water Quality Control Boards were examined for the facilities with large (>10 mgd) flows. Second, data on treatment level were compiled as part of a brine survey by the United States Bureau of Reclamation for coastal areas of southern California (USDI 2009). Third, the SWRCB wastewater user survey contains information on the treatment level of sewage agencies (CA SWRCB 2008). This database was matched based on the agency name in the California Integrated Water Quality System database. In some cases these databases disagreed, often because some facilities have a small water reclamation capability with advanced treatment, but the majority of the flow receives no advanced N removal treatment. In cases where the databases disagreed, the orders were assumed to be correct, followed by the United States Bureau of Reclamation report, followed by the SWRCB wastewater use survey. Facilities with no information were assumed to have no advanced treatment. The average N load removed from these facilities with advanced treatment was ~50% based on dividing the median inorganic N (NH3 + NO3

-) concentration of the facilities with treatment by the median NH3 concentration of facilities without treatment. Dissolved organic N is rarely measured by itself and was assumed to be a minor portion of the flow and unaffected by treatment. The decrease in inorganic N associated with advanced N removal was assumed to be converted to N2 gas through denitrification.

The fate of discharged wastewater N was based on the permit type and facility location.

Facilities with a National Pollutant Discharge Elimination System permit were assumed to discharge to surface water and are regulated by the United States EPA and subject to the federal Clean Water Act. Facilities with a NON15 Waste Discharge Requirement Program, regulated by the SWRCB, were assumed to discharge to land. If a facility had both permit types, the discharge was assumed to go to surface water. For facilities with National Pollutant Discharge Elimination System permits, the surface water body receiving the effluent is listed as part of the permit. In many cases, the receiving water body was the Pacific Ocean. In addition to facilities discharging directly to the ocean, facilities that discharged to San Francisco Bay, San Pablo Bay, Carquinez Strait, or Suisun Bay (as well as Sacramento and Stockton which discharge downstream of the river gauging stations on the Sacramento and San Joaquin Rivers) were also included in calculations of wastewater discharge to the ocean. In some cases, land applied effluent is applied



to fields growing crops, while in others it is applied to the surface of recharge basins. However, we assumed that all wastewater N discharged to land would flow completely to groundwater with no gaseous outputs or plant uptake after application. To calculate the N load in rivers associated with sewage discharge, a point vector layer of the georeferenced facility addresses was created and joined with the polygon layer of major (>1000 km2) watersheds in the state based on the United State Geological Survey Hydrologic Units in ArcGIS.

In addition to dissolved forms of N in effluent, wastewater treatment also results in the production of waste biosolids and gaseous forms of N. The two most common uses for the treated solids, or biosolids, are application as an organic amendment to soils, often in degraded areas, or use as an alternative daily cover in landfills. We assumed that all of the biosolids were used on urban land equally split between land application and landfills. The tonnage and fate of biosolids in the state were estimated by the California Association of Sanitation Agencies. The biosolids N content was assumed to be 3% (Tchobanoglous et al. 2002).

A small fraction of the wastewater N is emitted as N2O during treatment, which is tracked as part of the statewide greenhouse gas inventory by both the California ARB and the United States EPA. In addition, N2 can be produced most commonly in facilities that promote nitrification followed by denitrification during advanced wastewater treatment. Emission as N2 would be expected during advanced secondary or tertiary treatment (see above for calculations), but we assumed that no N2 was emitted in the absence of advanced N removal treatment.

According to the 1990 United States Census (United States Census Bureau 1992), approximately 10.4% of households in California were not on centralized sewage systems and the percentage with on-site waste treatment (i.e. septic systems) was essentially unchanged in 1999 (TCW Economics 2008). Based on Lauver and Baker (2000) we assumed that the N removal efficiency was 9%, which is already accounted for in the flow of biosolids from wastewater treatment plants. We assumed that the other 91% of the N from septic systems leached to groundwater.

Households produce other forms of N-containing waste besides sewage. Food waste was described earlier in this section, but a fraction of household and yard waste is disposed of in landfills. Surveys of the materials transported to landfills are conducted periodically by the California Department of Resources Recycling and Recovery. Landfill N disposal was calculated based on the tonnage of organic materials and their N content (Table 4.10 in CNA).

Household pet waste and feed intake requirements were calculated based on the average body mass of dogs (20 kg) and cats (3.6 kg) (Baker et al. 2001, NRC 2006)). Feed intake calculations assumed that all feed intake was excreted. Populations of dogs and cats for 2006 were



taken from AVMA (2007). We follow the approach of Baker et al. (2001) by assuming that 100% of dog waste and 50% of cat waste is added to urban soils. Ammonia emissions from dog (24%) and cat (12%) waste were from Sutton et al. (2000).

4.2.8 Gaseous Emissions

Gas emissions were tracked by individual gas (NOx, N2O, N2, NH3) for all sources. Fossil fuel combustion (section 4.2.1), upwind sources (section 4.2.2), manure (section 4.2.6), wastewater (section 4.2.7), and surface waters (section 4.2.9) all emit one or all of these gases, but are described elsewhere. This section provides the methods for gaseous emissions from soils and forest wildfires.

Total N volatilization during natural land fires was estimated as the product of average annual acreage burned (H. Safford, personal communication) and an average areal N emission rate of 100 kg N ha-1 during fires (Johnson et al. 1998). The emission of NOx and NH3 from fires was based on the 2005 EPA National Emission Inventory (EPA 2008) while N2O emissions were determined to be an insignificant flow based on the ARB greenhouse gas inventory (CARB 2010). The balance of the volatilized N was assumed to be N2.

Ammonia emissions for natural land soils were estimated from the biome-specific rates modeled by Potter et al. (2003) for California and extrapolated to the entire state based on the land cover map. Statewide emissions of NO and N2O from soils on natural land were scaled up with the land cover map using the average of published sources reporting typical biome-specific rates (Table 4.5 in CNA).

For cropland, unlike the natural land biomes, we also compiled published estimates of gaseous emissions in California. The only source of field NO emissions in California was the average daily flux of all crops reported in Matson et al. (1997). For N2O, the median rate was calculated across all crops and management practices for N2O emissions for California published in the last decade (Appendix 4.1). A second unique approach for estimating N2O emissions from cropland combined the estimate based on an emission factor for fertilizer with background emissions unrelated to fertilizer use. We assumed a direct emissions factor of 1% for both synthetic fertilizer and manure applied to cultivated cropland based on the ARB methodology in the greenhouse gas inventory (CARB 2010). However, we also include a background soil emission rate of 1 kg N ha-1 yr-1 (Stehfest and Bouwman 2006) in order to estimate total N2O emissions and not just anthropogenic emissions. This background rate is higher than most natural ecosystems, but there are no current estimates of N2O emissions in California cropland soils that don’t receive fertilizer. For both cropland and natural land, N2 emissions were based on



the mean N2:N2O ratios reported for natural land (1.03) and cropland (1.66) (Schlesinger 2009). Cropland NH3 emissions for synthetic fertilizer were based on the direct emissions factor reported in Krauter et al. (2006). On average, across the range of fertilizer types and crops with varying agronomic practices that were studied, 3.2% of applied synthetic fertilizer was volatilized as NH3, but emissions ranged from 0.1 to 6.5% of applied fertilizer. Based on the crop mix in California, Krauter et al. (2006) suggested that the actual emission factor was only 2.4%. While the emission factor for urea can be significantly higher, most other fertilizers are reported to have an emission factor of less than 5% (Battye et al. 2003). Using the values in Battye et al. (2003) and the reported sales of fertilizer in California during the study period, the emissions factor ranges from 4% to 5%. Ammonia emissions associated with manure application on cropland were based on the reported values for each class of livestock in EPA (2004), ranging from 3% for beef cattle to 15% for poultry.

For urban land, gaseous emissions were assumed to occur only from turfgrass soils related to fertilization. Gaseous emissions were based on data compiled in Petrovic (1990) on the direct emissions of fertilizer N. The median fraction of fertilizer that volatilized as NH3 or was denitrified in turfgrass areas was calculated for all the reported data. Total emissions were calculated based on the total synthetic N fertilizer use in urban areas.

4.2.9 Surface Water Loadings and Withdrawals

Only 55% of California’s land area drains to the ocean. This area does not include the Tulare Basin, which is now essentially a closed basin because of water management. The only point source of N to surface waters was the discharge of wastewater effluent as described in Section 4.2.7. We did not include any discharge of food processors to surface water. These facilities are regulated by Regional Water Quality Control Boards in either the stormwater program or in the wastewater program. To get a sense of the potential for discharge to surface water from food processors, we calculated total N discharge for the 162 facilities in the Central Valley included by HydroGeoPhysics Inc. as part of the Hilmar Supplemental Environmental Project (HydroGeoPhysics 2007). While many facilities do not have monitoring data, the sum of the loading from those that do was ~ 2 Gg N yr-1. Because of the lack of complete data for these discharges, we do not include them in the calculations. We estimate atmospheric N deposition on surface water bodies by summing the modeled CMAQ deposition (described in Section 4.2.2) for all of the surface water pixels in the land-use map.

Total loading to surface water from non-point sources was calculated based on the export coefficients for cropland (ECC = 11.9 kg N ha-1 yr-1), natural land (ECN = 2.4 kg N ha-1 yr-1), and



urban land (ECU = 9.3 kg N ha-1 yr-1) (Wickham et al. 2008). To check if these values were reasonable for California, we calculated export coefficients for 25 of the subwatersheds of the San Joaquin and Sacramento Rivers in the Central Valley measured by Kratzer et al. (2011) and the area of cropland, urban land, and natural land from our land-use map; we excluded two drainages as outliers (Colusa Basin Drain and Sacramento Slough). Using the Solver function in Excel, we calculated the best fit ECC, ECU, and ECN for the Central Valley. We then solved for the export coefficients by minimizing the sum of the squared difference between the measured and predicted yields with the predicted yield calculated as ECC * % Cropland + ECU * % Urban Land + ECN * % Natural Land. Similar to Wickham et al. (2008), we estimated ECC = 14.2 kg N ha-1 yr-

1, ECN = 1.6 kg N ha-1 yr-1, and ECU = 7.0 kg N ha-1 yr-1. The N loading to the ocean was estimated in two distinct ways. First, for the major

watersheds (>1000 km2) where measured N discharge has been reported, we used the measured values from Sobota et al. (2009), Schaefer et al. (2009), and Kratzer et al. (2011). In watersheds where measurements had not been made, we used adjusted estimates from the export coefficients. The export coefficients provide a means to predict N loading to surface water, but not necessarily the N discharge to the ocean because of gaseous emissions and sedimentation in reservoirs. We calculated the log-log relationship between the measured values and predicted values for the eight watersheds with measured data. We used the regression of this relationship log [ (Measured N) = 0.5685 * log (Predicted N) + 1.2991 (R2=0.71) ] for these ungauged watersheds to adjust the predicted N discharge from the export coefficients to predict the actual discharge of N. We report the values predicted by the export coefficients, the adjusted values predicted by the export coefficients and the measured values for the watersheds in the state (Table 4.15 in CNA). Nitrogen loads for the urbanized areas in the San Francisco Bay watershed and along the southern coast from Santa Barbara to the Mexican border were estimated in Davis et al. (2004) and Ackerman and Schiff (2003), respectively. However, in both cases the estimates are for stormwater inputs of inorganic N only, so they likely underestimate the total N load.

Water withdrawals for irrigation were considered an output from the surface water subsystem. The volume of water for irrigation was based on Hutson et al. (2004), which reported 26*1012 L yr-1 withdrawn for California in 2000. An average of 7.8*1012 L yr-1 of this water was pumped from the Delta from 2000-2004. The water pumped from the Delta was not included in the surface water mass balance as it was actually considered a N import to the state because of the location of USGS river gauges. That is, for the purposes of our N budget, the Delta pumps are located outside of the study area, so that the dissolved N in this water is considered a N import to the state. The water quality at the Harvey O. Banks Pumping Plant (Station number KA000331),



where water is pumped from the Delta, was historically monitored each month (CA DWR 2013). The total N concentration for 2002-2007 was on average ~1 mg N L-1, and was split almost evenly between nitrate and dissolved organic N. The N concentration was assumed to be the same for the 18.2*1012 L yr-1 withdrawn from other surface water bodies in California. A smaller volume of surface water was withdrawn for domestic use (4.6*1012 L yr-1): we ignored this flow as the majority of this water is used for indoor residential and industrial use which would likely be accounted for in wastewater effluent to surface water or the ocean (Gleick et al. 2003).

Gaseous outputs from surface water were only significant in the form of N2 and N2O, predominantly from denitrification. For rivers, gas emissions were estimated based on the areal rates of 2.8 kg N2O-N ha-1 (Beaulieu et al. 2011) and 51 kg N2-N ha-1 yr-1 (Mulholland et al. 2009).

The gaseous emissions from lakes and reservoirs were also based on these sources given the similarity in denitrification rates in rivers and lakes reported in Seitzinger et al. (2006). The acreage of rivers, lakes and reservoirs was based on a comparison between the USGS National Hydrography Dataset and the CAML land-use map. Waterbody pixels in the land-use map not identified as lakes or reservoirs in the USGS dataset were categorized as rivers.

The burial of N in lake and reservoir sediments was considered surface water storage and was estimated by difference for the purposes of the mass balance. However, there are two potential independent approaches to calculating the amount of N retained. The first provides an estimate for just reservoirs, and the second, for both lakes and reservoirs. First, the total volume of sediment in all California reservoirs was estimated by Minear and Kondolf (2009). Based on the reservoir age, an annual sedimentation rate was calculated. The annual rate of N sedimentation was calculated by assuming a bulk density of 1 g cm-3 (Verstraeten and Poesen 2001), a carbon content of these sediments of 1.9% (Stallard 1998) and a C:N ratio of 10 (Vanni et al. 2011). Second, Harrison et al. (2008) estimated that a global average of 306 kg N ha-1 yr-1 was retained in reservoirs. These authors also estimated that lakes retain ~30 kg N ha-1 yr-1. The total annual N retention was calculated from the area of reservoirs (180,000 ha) and lakes (350,000 ha) in the state by partitioning the National Hydrography dataset. The difference between retention and denitrification as calculated above provides an estimate of burial in sediments.

4.2.10 Groundwater Loading and Withdrawals

Groundwater inputs included leaching from septic tanks and wastewater treatment discharge (Section 4.2.7), cropland soils, and natural land soils. For cropland, leaching to groundwater was calculated in two ways. First, the average

NO3- concentration in water leached below the rooting



zone in crop soils was calculated from a compilation of California literature (See Chapter 7 for details on data). The N concentration (38 mg N/L) was multiplied by the total volume of recharge in agricultural regions, where the majority of groundwater recharge occurs. All of the recharge was assumed to occur in the Central Valley (9.6*1012 L; Faunt 2009), Salinas Valley (2.3*1011 L; Montgomery Watson 1997) and Imperial Valley (3.0*1011 L; Montgomery Watson 1995) groundwater basins. Second, the median fraction of applied fertilizer that leached was calculated from a compilation of California literature (see Chapter 7 for further details on data).

This fraction (38%) was multiplied by the sum of statewide fertilizer use in cropland (synthetic fertilizer + manure). In natural land, groundwater inputs were assumed to occur only in areas lacking drainage to the ocean. Leaching inputs in the driest portions of the state which occur in closed basins have been estimated based on the N stock in the subsurface that has accumulated over millennia. The annual N flow was calculated as the product of a leaching rate of 0.6 kg N ha-

1 yr-1 (Walvoord et al. 2003) and an area of 18 million ha. Leaching from turfgrass was estimated as the median of the fraction of applied fertilizer that leached as summarized by Petrovic et al. (1990).

Groundwater outputs were only from water pumped from the ground. Nitrogen removal from groundwater was calculated as the product of groundwater volume withdrawn and average groundwater N concentration. The volume of groundwater withdrawal was reported in both Hutson et al. (2004) and CA DWR (2003). However, we used the former for the calculations because it partitioned use into municipal vs. irrigation and also provided estimates of surface water withdrawals. N concentrations were calculated as the average of all wells available in the USGS Groundwater Ambient Monitoring and Assessment and EPA STORET databases for the years 2002-2007 available on the Geotracker website (CA SWRCB, n.d.).

We calculated groundwater denitrification in three ways. (1) We estimated N inputs to groundwater since 1940 and used literature values for the half-life of N to estimate denitrification losses. Green et al. (2008) report a half-life of 31 years at one site near Merced. These authors found limited evidence for denitrification in aquifers below cropland soils in California, with 50% N removal in groundwater after 31 years. This represents a loss rate of 2.3% yr-1. A second estimate of the half-life can be made from the 3H/He and N2 excess reported in Landon et al. (2011). The data from this study, which covered a much larger area of the Central Valley, would result in a half-life of 80 years or a loss rate of only 0.9% yr-1 (C. Green, personal communication). Because of the more regional nature of this study, we chose the value calculated from Landon et al. (2011). We assumed that groundwater recharge of N has increased linearly since 1940 with only 10 Gg N of natural inputs occurring prior to 1940. We chose this starting



date based on the trend in fertilizer use (sales of synthetic fertilizer plus dairy manure since 1980). Manure production was assumed to start in 1980 because dairies had largely transitioned to confined feeding by then. Manure production was calculated based on milk production reported by USDA NASS (2012) with an assumed efficiency of 25%. Manure applied as fertilizer was calculated assuming 38% of manure production was volatilized. The x-intercept of the fertilizer-time relationship was 1940. Finally, groundwater N extraction was assumed to be zero in 1940 and increased linearly to 2005. Starting in 1940 10 Gg N was leached, 0 Gg N was extracted, 0.23 Gg N was denitrified, and 9.67 Gg N was stored. This process was assumed to continue with 0.9% of the annual input plus the groundwater storage denitrified annually. (2) We used the product of a concentration-based denitrification rate and the total volume of groundwater. Liao et al. (2012) reported denitrification in Merced County to be 0.2 mg N L-1 yr-1. Based on the data in Landon et al. (2011), a more regional value of groundwater denitrification was estimated to be 0.04 mg N L-1 yr-1. The volume of recharge water contaminated with N was assumed to be constant between 1940 and 2005 and was estimated the same way as for determining the load of N leaching from soils. (3) We used the average proportion of groundwater N inputs that were denitrified as reported for Europe (46%; Leip et al. 2011) and globally (40%; Seitzinger et al. 2006). The groundwater denitrification was the average of the three independent estimates.

We assumed that the net N exchange between groundwater and surface water was essentially zero. For the Central Valley aquifer, if anything, the flow of water (0.2*1012 L yr-1) moves from surface water to groundwater (Faunt 2009). At a N concentration of 1 mg N L-1 as measured in the Delta representing the water in the Sacramento and San Joaquin rivers, this represents an insignificant flow of N. Nitrogen storage was calculated as the difference between inputs and withdrawals. 4.2.11 Storage

Storage in cropland and natural land subsystems was calculated by difference. That is, storage was equal to the difference of N flows in and out. This storage could occur in either soils or perennial vegetation. Storage in urban systems has three components. First, landfills are considered storage and the methods of calculating N flows to landfill are described in Section 4.2.7. Second, land (soils + vegetation) storage was calculated as the difference between inputs of fertilizer, atmospheric deposition, and dog waste and the outputs in the form of soil gaseous emissions and surface runoff. Finally, other storage was calculated as the difference between synthetic chemical and wood N inputs and landfill N storage.



The storage terms calculated for the surface water and groundwater subsystems are described in Sections 4.2.9 and 4.2.10, respectively.

Box A4.2.1 The Haber-Bosch process and cropland nitrogen

Synthetic fertilizer, which is almost exclusively produced by the Haber-Bosch process, is the largest source of N to cropland. However, Haber-Bosch derived N is not limited to the annual application of synthetic fertilizer. The N in applied manure also originates in part from feed that was grown with synthetic fertilizer and in part from biological N fixation by alfalfa. Of the 537 Gg N yr-1 needed to feed livestock, 170 Gg N yr-1 of the feed was in the form of alfalfa, Thus, alfalfa contributed 30% of the N supply in livestock feed, and presumably an equivalent fraction of manure N. The remaining manure (184 Gg N yr-1) presumably originates as Haber-Bosch N. A large fraction of the biosolids applied to cropland also comes from Haber-Bosch N. The N applied in irrigation water could originate from any land use, but synthetic fertilizer application to cropland is likely the dominant source. Atmospheric deposition is a mixture of fossil fuel combustion with some contribution of reduced N from livestock manure NH3 volatilization. If we assume that irrigation water was derived from synthetic N while atmospheric deposition was fossil fuel combustion, a total of 69% of N entering the cropland subsystem (Figure 4.4 in CNA) was from synthetic N fixation. At the statewide level, there is also the import of grain crops, largely corn, to California from the Midwest that is largely Haber-Bosch N as well.



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CHAPTER SIX

Scenarios for the Future of Nitrogen Management in California

Appendix 6.1 Roster of Stakeholder Participants from Workshops

Lead Authors:

S. BRODT AND T.P. TOMICH


J. BARNUM, C. BISHOP, AND G. HARRIS



Suggested citation:

S Brodt, TP Tomich, J Barnum, C Bishop, and G Harris. “Appendix 6.1: Roster of Stakeholder Participants from Workshops.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 6.1 ROSTER OF STAKEHOLDER PARTICIPANTS FROM WORKSHOPS 2

6.1. Roster of stakeholder participants from workshops

The workshops were conducted on the UC Davis campus (June 9 and 10, 2010; and September 23, 2010), and facilitated by Gerald Harris and Jeff Barnum of Reos Partners. Pelayo Alvarez, California Rangeland Conservation Coalition Program Director, Defenders of Wildlife Ted Batkin, President, Citrus Research Board Steve Beckley, Executive Director, Organic Fertilizer Association of California (OFAC) Colin Bishop, Communications and Outreach Fellow, Agricultural Sustainability Institute (ASI) at UC Davis Don Bransford, Chairman, CA Rice Producer's Group, California Rice Commission Sonja Brodt, Coordinator for Agriculture, Resources, and the Environment, Agricultural Sustainability Institute (ASI) at UC Davis Paul Buttner, Manager of Environmental Affairs, California Rice Commission Antoine Champetier, Policy Options Fellow, Agricultural Sustainability Institute (ASI) at UC Davis Jennifer Clary, Program Associate, Clean Water Action Cynthia Cory, Director, Environmental Affairs, California Farm Bureau Federation (CFBF) Bob Curtis, Associate Director of Agricultural Affairs, Almond Board of California Michael Dimock, President, Roots of Change Rex Dufour, Program Specialist, National Center for Appropriate Technology Allen Dusault, Program Director for Sustainable Agriculture, Sustainable Conservation Laurel Firestone, Co-Director, Community Water Center (CWC) Hank Giclas, Sr. Vice President, Strategic Planning, Science and Technology, Western Growers Association Larry Glashoff, Hort Tech Manager, Hines Nurseries Joseph Grant, Farm Advisor, University of California Cooperative Extension (UCCE), San Joaquin County Ian Greene, Research Programs Manager, California Strawberry Commission Edward Hard, CDFA Fertilizer Research and Education Program (FREP) Don Hodge, Environmental Protection Specialist, US Environmental Protection Agency (US EPA) Tim Johnson, President-CEO, California Rice Commission


APPENDIX 6.1 ROSTER OF STAKEHOLDER PARTICIPANTS FROM WORKSHOPS 3

David Lighthall, Health Science Advisor, San Joaquin Valley Air Pollution Control District Dan Liptzin, Biogeochemistry Fellow, Agricultural Sustainability Institute (ASI) at UC Davis Karl Longley, Coordinator of Water Resources Programs, California Water Institute Jim Lugg, Consultant, Fresh Express/Chiquita Paul Martin, Director of Environmental Services, Western United Dairymen Albert Medvitz, McCormack Sheep and Grain Rob Mikkelsen, Western Regional Director, International Plant Nutrition Institute (IPNI) Belinda Morris, Regional Director for Conservation Incentives, Environmental Defense Fund (EDF) Stephanie Ogburn, Communications and Outreach Fellow, Agricultural Sustainability Institute (ASI) at UC Davis Renee Pinel, President/CEO, Western Plant Health Association Belinda Platts, Agricultural Consultant, ret. Production Manager, Dole Fresh Vegetables Claudia Reid, Policy and Program Director, California Certified Organic Farmers (CCOF) Bruce Rominger, Owner, Rominger Brothers Farms Todd Rosenstock, Best Practices and Technical Options Fellow, Agricultural Sustainability Institute (ASI) at UC Davis David Runsten, Policy Director, Community Alliance with Family Farmers (CAFF) Steve Shaffer, Environmental Consulting for Agriculture Tom Tomich, Director, Agricultural Sustainability Institute (ASI) at UC Davis

CHAPTER SIX

Scenarios for the Future of Nitrogen Management in California

Appendix 6.2 Background and Process

Lead Authors:

S. BRODT AND T.P. TOMICH


J. BARNUM, C. BISHOP, AND G. HARRIS



Suggested citation:

S Brodt, TP Tomich, J Barnum, C Bishop, and G Harris. “Appendix 6.2: Background and Process.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 6.2 BACKGROUND AND PROCESS 2

6.2. Background and Process

Scenario analysis is a widely used process to create plausible stories despite uncertainties about the future. The process allows decision makers to better see and understand the implications of decisions that have or could have long term effects on their organizations or other interests. It also creates opportunities for different stakeholders to learn from an informative negotiation process among their diverse perspectives, and to suggest strategies for addressing problem issues.

The scenarios for this project were focused on the issue of N management in California agriculture. While N plays a central and critical role in crop and livestock production, N use has led to unintended consequences, among which are greenhouse gas emissions and ground water pollution. Stakeholder participants devised a set of scenarios as a means to create a big-picture view leading to a more comprehensive understanding of response options regarding California’s N management and how these responses might affect farm profitability as well as environmental and human health outcomes over time.

Once a set of scenarios is created, it can be used to brainstorm and test potential responses to emerging conditions. Scenarios allow a proactive approach to planning; they allow stakeholders to consider options and prepare for actions in advance of a future event or situation. Further, scenarios can help identify early indicators and significant outliers.

In addition to the role scenarios can play in looking at the future, the California Nitrogen Assessment scenario process was designed to increase awareness and understanding across the assessment’s diverse stakeholder groups, and to ensure that a wide variety of perspectives were heard. This process was facilitated by Gerald Harris and Jeff Barnum of Reos Partners, who began working with the assessment team in April 2010. Stakeholders were contacted that same month regarding their availability for future workshops, and given the opportunity to participate in pre-workshop interviews. Those interviews were conducted face-to-face and via telephone by Harris and Barnum in May 2010, and input from those interviews was used to shape the workshops.

During the first workshop session (June 9 and 10, 2010), stakeholder participants identified a number of important drivers that would be likely to influence the future use of N in California agriculture. The facilitation team captured a list of these factors and grouped them into seven major categories:

• Technological change • Changes in farming economics (profitability)



• Advances in N cycle understanding • Awareness of the impact of N on human health and the environment • Changes in the energy system aspects of agriculture • Shifts in public policy related to managing N impacts in California • Information creation and dissemination

Through group discussion, participants then jointly agreed on two driving forces from

this list of categories to serve as the primary variables for the four scenarios stories, following a general model from other scenario development efforts (Henrichs et al., 2010; Schwartz, 1996; Van’t Klooster and van Asselt, 2006). The two attributes were chosen because they were simultaneously highly uncertain and highly important—changes in farming profitability and shifts in the public policy of N management. Participants agreed by a wide margin that these two factors are most uncertain and most important, and will thus most significantly affect how N-use decisions will be made in California agriculture over the next twenty years. Participants identified economic conditions that affect the viability of farms as vitally important, especially because of the wide diversity of different crops grown in California. They also agreed that public policy and regulation are central because they directly affect operating decisions and allow issues important to both government and consumers to be incorporated into agriculture. The extreme ranges of uncertainty of these two drivers help to differentiate the four possible scenarios from one another. The scenarios reside within the four quadrants created by these two drivers, with external forces driving changes in farming profitability representing the horizontal axis and shifts in public policy representing the vertical axis.

Many of the drivers discussed by the scenarios workshop group are similar to the drivers identified by the nitrogen assessment (see chapters 2 and 3). These include: global food systems, population and economic growth, regulations and incentives, land value, development of new technology, fossil fuel combustion, land-use conversion, and farm management (for both plant and animal systems).

After selection of drivers, the workshop participants were divided into four groups, with attention to representation of different stakeholder categories in each group. One or two members of the assessment project team were also present in each group as equal participants (i.e. they did not adopt particular leadership roles within the groups). Each group was assigned one of the four quadrants to use as a basis for developing a scenario storyline. Through group discussions, participants developed storylines in seven-year increments that were captured in notes written by one or two group-selected members on flip charts. At the end of the multi-hour



session, each group took a turn to orally present its scenario storyline to the entire workshop group, with workshop facilitators taking notes. The facilitators, with input from the assessment team, then used their own notes plus each group’s notes to write out scenario storylines in text form. Members of the assessment team checked the storylines for plausibility and consistency.

In September 2010, stakeholders reconvened at a second workshop to review the core ideas of the four scenarios previously developed, discuss any disagreements or alternative interpretations for the scenario storylines written by the facilitators and assessment team, identify gaps and additional drivers and outcomes, and suggest any necessary revisions. The group also discussed how the scenarios affect policy and agricultural practices (see Section 6.6) and possible research topics for the assessment which would provide needed information for varying audiences.

Members of the assessment team made final edits to the storylines based on the second workshop and re-checked all storylines for plausibility and consistency. This process led to some simplification and small changes in specific details contained within the storylines, but did not result in any fundamentally different outcomes for any of the four scenarios. References Henrichs, T., Zurek, M., Eickhout, B., Kok, K., Raudsepp-Hearne, C., Ribeiro, T., van Vuuren,

D., Volkery, A., 2010. Scenario development and analysis for forward-looking ecosystem assessments, in: Ash, N., Blanco, H., Brown, C., Garcia, K., Henrichs, T., Lucas, N., Raudsepp-Hearne, C., Scholes, R., Simpson, R.D. (Eds.), Ecosystems and Human Well-Being: A Manual for Assessment Practitioners. Island Press, Washington.

Schwartz, P., 1996. The Art of the Long View: Planning for the Future in an Uncertain World. Doubleday, New York.

Van’t Klooster, S.A., van Asselt, M.B.A., 2006. Practicing the scenario-axes technique. Futures 38, 15–30.

CHAPTER SEVEN

Responses: Technologies and Practices

Appendix 7.1 Technical options to control the nitrogen cascade in California agriculture

Lead Authors:

T.S. ROSENSTOCK


S. BRODT, M. BURGER, H. LEVERENZ, D. MEYER



Suggested citation:

TS Rosenstock, S Brodt, M Burger, H Leverenz, and D Meyer. “Appendix 7.1: Technical options to control the nitrogen cascade in California agriculture.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 7.1 TECHNICAL OPTIONS TO CONTROL THE NITROGEN CASCADE 2

7.1.0 Technical Options to Control the Nitrogen Cascade In California Agriculture

This appendix describes the scientific basis, capacity, and applicability of management practices and technologies used to manage nitrogen (N) in California agriculture1. Countless methods have been developed to this end; the discussion here is not intended to be an exhaustive review. Instead, we direct attention toward N management approaches that have one or more of the following characteristics: are commonly used, have high potential to mitigate N effects, are receiving some research attention but have uncertain effects, have the potential for unintended consequences by transferring N from one medium to another, or were of particular interest to various stakeholder groups (Box 7.1.1). Additional information on N management in agriculture and the mechanisms to manage N from other drivers (e.g., industry) can be found in the resources listed in Table 7.1.1.1.

1Engineering technologies used to control N emissions due to fuel combustion and waste management are transferable, well established, and covered in depth in other texts. Therefore, the discussion here focuses solely on agricultural N management.

Box 7.1.1 Why A Qualitative, Not Quantitative, Assessment

The California Nitrogen Assessment takes a qualitative and not quantitative approach to its assessment of individual agricultural management practices’ and technologies’ capacity to regulate the N cascade. A qualitative assessment was justified for two reasons. First, California production conditions are unique, both in climate and management. Site characteristics significantly influence the fate of N and the efficacy of any practice. Extrapolation from research from other areas is not necessarily appropriate. With the limited research under California conditions, and even smaller evidence pool when considering the dramatic changes in production in the last 20 years, it is more reasonable to evaluate the potential effectiveness of practices from a theoretical perspective than an empirical one. The second and perhaps more important reason is that management practices and technologies are not distinct. Interactions among practices make it challenging to quantitatively isolate the effects of a given change in management. Reductionist research can help with this. However, farmers implement practices and technologies in bundles. Multiple factors may be changed simultaneously and have synergistic or antagonistic effects on N flows. Therefore estimates of the impact of a single change are meaningless, in practice.



Table 7.1.1.1. Resources Describing Technical Options to Control the Nitrogen Cycle from Agricultural and Non-Agricultural Sources

Source Activity References Fuel Combustion EPA (1999), Pereira and Amiridis (1995), Skalska et al., (2010) Wastewater treatment plants EPA (2008a, 2008b), Metcalf and Eddy (2003) Onsite wastewater management Leverenz and Tchobanoglous (2007) Agriculture Dzurella et al. (2012), Eagle et al. (2010), Hristov et al. (2011)

7.1.1 The Nitrogen Cycle

Understanding the potential efficacy of different management interventions in regulating the nitrogen (N) cycle requires knowledge of N cycling processes. Through management, producers modify the quantity of reactive N available and conditions of the soil environment. By changing the substrate quantity and soil biological, chemical, and physical properties, they alter the tendency for and pace of microbial N transformations, plant uptake, chemical conversions, and emissions. It is the ability to impact these processes that create opportunities to control the N cascade2. Descriptions of the forms of N and the major processes of the N cycle can be found in Table 7.1.1.2. Table 7.1.1.2 Major Nitrogen Cycling Processes

Process Description Controlling Factors

Mineralization Conversion of organic N in soil, crop residues or manure into inorganic forms

Temperature, water content

Nitrification Two step conversion of NH4 to NO3 via NO2 Temperature (< 50 degrees nearly stops), water content, oxygen

Immobilization Conversion of inorganic N to organic N. Occurs when microorganisms decompose materials with high C/N ratio. Decreases plant available N

Carbon

Volatilization Release of NH3 in gaseous form to the atmosphere

pH, temperature, wind speed

Denitrification Bacteria convert NO3 to N2 gas; use NO3 instead of oxygen in metabolic processes in low oxygen

Oxygen, temperature, water-filled pore space,

2 For a description of the N cascade, see introduction of Chapter 7.



environment carbon

Leaching Downward percolation of NO3 through soil profile; physical event where soluble NO3 moves by mass flow with drainage water

Soil water content, hydraulic conductivity, soil texture

Actions to regulate N dynamics affect the amount of reactive N in the environment through one of six mechanisms: conservation, substitution, transformation, source limitation, removal, or improved efficiency (EPA SAB, 2011). Examples include constructing wetlands to intercept NO3

- in runoff (removal), using nitrification inhibitors to retard conversion of NH4 to NO3

- (transformation), and improving distribution uniformity to increase the efficiency of irrigation and avoid saturating some parts of the field and thereby reducing oxygen availability (improved efficiency). Applicability of each strategy is subject to the constraints of the production environment (Table 7.1.1.3). Often there are multiple approaches available to modify N for a given combination of flow and production environment, with the best strategy emerging from the optimization of several factors including, but not limited to: availability of technology, cost, effectiveness, relevance to crop or animal species of interest, soil, irrigation system, regulations, climate, labor, and the market.

Table 7.1.1.3. Strategies to Control the Release of N Into the Environment. Source: Adapted from EPA SAB 2011.

Control Strategy Advantages Limitations Current Examples Improved practice and conservation

Decreases one or more emissions

Education costs, slow adoption, may increase other emission pathways

Tightly coupled water and nitrogen management in cropping systems

Product substitution

Decreases demand for N

Technological concerns, social acceptability

Use of biosolids and urban green wastes on croplands

Transformation

Reduces emissions May increase other N emissions

Use of biological nitrification/denitrification at wastewater treatment plant (tertiary treatment)

Source limitation

Reduces emissions

Requires large changes in societal behavior

Use of carpooling and high occupancy vehicle lanes



Removal

Reduces impacts Costly, dealing with by-product of removal is problematic

Treatment of NO3 contaminated drinking water, selective catalytic reduction in stationary fuel combustion sources

Improved efficiency Increased output per unit of N, may reduce need if output remains constant

Usually entails significant costs to implement

Feed management in dairy systems

7.1.2 Inorganic Nitrogen Management

Nitrogen (N) management refers to four, not mutually exclusive, decisions regarding the rate, source, timing, and placement of fertilizing materials. The canonical objective of N management, whether inorganic or organic, is to match the availability and supply of N with crop demand as closely as possible3 (Cassman et al., 2003; Ladha et al., 2005). Synchronizing supply and demand results in high fertilizer use efficiency and decreases pollution potential (Dobermann, 2005). In practice, however, plant availability of inorganic N, assimilation by roots, and gaseous and water-borne emissions are a function of a multitude of biological and chemical processes whose rates vary across space (fields, farms, and landscapes) and time (days, months, years) and are subject to a series of constraints ranging from climate to cultivars to cultural practices. A grower is, thus, faced with balancing complex and variable relationships between biology and technology. The challenge of managing these complex relationships underlies the efficiency, and inefficiency, of N fertilizer use in California.

7.1.2.1 Reduce Nitrogen Application Rates4

Crop production in California requires the addition of N fertilizer to supplement indigenous soil reserves. Simply put, applying N fertilizer to the soil turbo charges the N cycle: Microbial activity increases and the many N transformations that are mediated by microbes accelerate.

3 It is important to understand that it is practically impossible to perfectly match soil N supply with plant demand. Growers must add more fertilizer N than the plant takes up to maintain high levels of productivity. 4 The quantity of fertilizer used is called the “application rate” or “rate,” for short.



Amplification of the biological processes plus the comparatively greater magnitude of N in the system following fertilizer application catalyzes plant growth, but is also responsible for additional emissions risk. It is well established that yields increase along with N application rates until a threshold is reached where N no longer limits production, at which point, productivity plateaus or even declines (Cassman et al., 2002). Constraints and uncertainties inherent to farming—due to technology, information, economics, and weather limitations—often induce crop producers to supply more N than a crop assimilates to ensure adequate nutrition and high yield. Because of the surplus N use that often results, reducing the rate of application is an often-cited option to control emissions without compromising yield. Reducing N application rates limits the introduction of new N into the system and should decrease NO3

- leaching and gaseous emissions of nitrogenous compounds. The relationship between emissions and N rate is typically inverse to that of productivity and N rate. Research on N2O and NO3

- losses suggests emissions remain low, only slightly elevated above background levels, until a threshold is reached, near the season maximum amount of N taken up. After the N rate threshold is exceeded, pollution increases exponentially (Hoben et al., 2011; Millar et al., 2010; van Groenigen et al., 2010; Venterea et al., 2011). According to a meta-analysis of 18 studies, once N application rates exceed 11 kg per ha greater than plant uptake, N2O emissions increase exponentially for marginal additions of N fertilizer (van Groenigen et al., 2010). Similar relationships have been suggested for leaching and N fertilizer applications (Broadbent and Carlton, 1978). What this research suggests is that incremental reductions in N applied may have multiplicative effects on emissions, assuming N additions exceed plant uptake in the cropping system. Although the precise inflection point will be determined by edaphic soil, crop, and management factors including irrigation efficiency, carbon (C) availability, and timing and placement of fertilizer applications, identifying a threshold provides a metric for growers and custom fertilizer applicators to target. Changes in N application rates have the potential to decrease yields. Lower productivity may result from either insufficient quantities of N throughout the year, as might occur under ideal growing conditions that induce rapid crop growth and development, or unavailability of N during critical phenological periods. Part of the reason growers apply N at higher than needed rates is to hedge against such risks (the “insurance” hypothesis). Nevertheless, widespread over-fertilization has been documented in some California crops (Breschini and Hartz, 2002; Hartz et al., 2000; Johnstone et al., 2005). Under these conditions, N applications could be reduced without jeopardizing productivity or economic solvency. For example, Hartz et al. (2007) surveyed 78 fields of iceberg and romaine lettuce and found that the average N application rate



was 184 kg per ha but ranged between 30 to 440 kg per ha. Current University of California (UC) guidelines suggest that an application rate between 196 to 240 kg per ha is sufficient for these crops under most production conditions (Chapter 3). Even though the N rate varied by more than 300 kg per ha, yields were not correlated with N rate, suggesting misapplication on many sites. Less is known about the potential for over application of fertilizer N in perennial and field crops. One of the only recent surveys of N management practices in perennials did not ask about common N rates in nut crops (Lopus et al., 2010). Hartley and van Kessel (2003) document N rates in rice production. According to their survey, average application rates are within the range of guidelines. Overall, average producers of 5 of 12 vegetable crops and 4 of 12 perennial crops, but 0 of 5 field crops apply more N than the maximum rate suggested in the UC guidelines, suggesting there may be opportunities for reducing fertilizer N rate on many crops (Appendix 3.2 and 3.3). Clearly some crops are systematically fertilized excessively. But even for crops that are generally not, potential rate reductions are plausible simply because of the wide ranges in N application rates among fields and farms. Reducing rates requires more intensive management. Using a N management program that involves diagnostic testing to guide split N applications was shown to reduce N application rates by 60 to 112 kg per ha (approximately 30% of N applied) by comparison to industry standard fertilization practices in processing tomatoes (Hartz et al., 1994a). Although the results likely significantly overestimate potential reductions at this time due to recent meteoric increases in tomato yields and N uptake with adoption of micro-irrigation (Hartz and Bottoms, 2009), they are illustrative of the conceivable capacity to better target N decisions. For growers to reduce rates, information on crop demand and the technology to supply N are critical inputs to guide growers’ decisions on when, where, and how much to apply. The two primary tools California producers currently use to guide fertilizer N rate decisions are soil and tissue tests. Soil tests provide an indication of the amount of mineral N in soil and availability to plants. Tissue tests, in contrast, indicate the sufficiency or deficiency of N within the plant. Extensive research in vegetable crops has proven the value of soil tests for N decision-making (Breschini and Hartz, 2002; Hartz et al., 1994b). Comparatively, the utility of tissue sampling in perennial crops has been called into question recently (Brown personal communication). Antiquated sampling protocols that do not adequately account for spatial and temporal heterogeneity of soils or crop processes (Rosenstock et al., 2010) and “critical sufficiency values”5

5 Critical values refer to the concentration of nutrients within plant tissue. They are experimentally derived and reflect nutrient concentrations at a specific time of the year. See Embleton and Jones (1974) and Lovatt (2001) for examples of those in development and still in use.



established for cultivars and conditions unrepresentative of agriculture today limits the applicability of tissue tests in many situations. Furthermore, the ability to apply split applications and deliver precise fertilizer rates varies by cropping system and management, and in fertigation systems is affected by the distribution uniformity/irrigation efficiency of irrigation technology used and its management. In some cases, the size of the field and the economics of repeated management may preclude increased number of applications and better timed/even delivery of nutrients.

7.1.2.2. Change Inorganic Nitrogen Fertilizer Sources

Individual reactive N species are more or less susceptible to microbial transformations, adhesion to soil clay particles, or chemical conversion. Selection of a N source that promotes or suppresses specific N cycle attributes is thus theoretically possible. Options available to change inorganic N sources include: (1) switching between conventional materials (e.g., from urea or anhydrous ammonia to ammonium sulfate, or (2) switching from conventional synthetic materials to “enhanced efficiency materials.” 6 Changing between conventional materials can be an effective strategy to reduce NH3 volatilization losses, and N2O emissions. Recall that volatilization is a physiochemical reaction of soluble NH4 being converted to gaseous phase. Thus, fertilizers that contain NH4 or hydrolyze easily to this compound (e.g., urea) will have considerably higher emissions, especially when applied to the soil surface. Harrison and Webb (2001) conclude from their review of the literature that emission rates from urea-based fertilizer often exceed 40% of N applied while rates from ammonium nitrate are an order of magnitude lower. Limited use of urea and widespread use of mixed ammonia and nitrate fertilizer blends are reasons volatilization from current California cropping systems that use chemical fertilizer accounts for a relatively insignificant N flow. Recent empirical results show that only an average of 3% of N applied is given off as NH3 under California production condition (Krauter and Blake, 2009).

6 Enhanced efficiency fertilizers (EEF) are synthetically derived materials that are engineered to moderate the rate at which N becomes available to plants and microbes, extending it over a longer period of time (Shaviv and Mikkelsen, 1993). They achieve this by either building protective shells around solid fertilizer that dissolve (e.g., sulfur coated) or using chemicals that retard microbial action (e.g., nitrification inhibitors). The nature of the material itself and environmental conditions—namely temperature and soil moisture—determine the rate of N release, with N being released more rapidly under hotter, wetter conditions. It is important to note that a wide range of EEF are available in the marketplace—from nitrification inhibitors to polymer coated urea—and their mode of action in the soil is different.



Changing fertilizer type can also have an effect on N2O flux. After reviewing more than 1000 studies worldwide of N2O production, Stehfest and Bouwman (2006) conclude that rates of N2O evolution from anhydrous ammonia are significantly higher than from other fertilizer types, even when accounting for differences in the experimental conditions across studies, such as tillage systems, fertilizer placement, soil C, and pH (Snyder et al., 2009). Burger and Venterea (2011) reviewed only side-by-side comparison trials from North America (none included California conditions), and likewise found that in five out of six studies, anhydrous ammonia was the most likely to generate significantly higher N2O emissions, compared to urea-based fertilizers. In addition, two recent California studies in corn and wheat found that use of ammonia fertilizers (in anhydrous or aqua forms) generated from 40 to 60% more N2O emissions compared to urea-based or sulfate fertilizers (Burger and Waterhouse, 2016; Zhu-Barker et al., 2015). Switching to enhanced efficiency fertilizers (EEF) from conventional synthetic fertilizers is often widely considered a valuable technological option to address the N challenge (Akiyama et al., 2010; Halvorson et al., 2010). Data suggest EEF are effective at reducing N losses. A recent meta-analysis of the efficacy of EEF to regulate N2O emissions demonstrates that polymer coated fertilizers and nitrification inhibitors decrease N2O by 35% and 38%, respectively (Akiyama et al., 2010). But the results of the research on EEF and N2O may be confounded by experimental design. Some evidence suggests that although EEF present lower initial fluxes, N2O production may extend for longer periods and therefore may show higher total losses (Delgado and Mosier, 1996) or similar total annual losses (Parkin and Hatfield, 2010) when compared to fertilizer application without nitrification inhibitors. Nitrate leaching potential may also be reduced with the use of EEFs. With its negative ionic charge and water solubility, NO3

- does not adhere to similarly charged clay particles and therefore are not readily retained in the soil matrix. Nitrate readily leaches below the root zone with water, especially when the soil profile is near saturation, which can occur with uneven distribution of irrigation water or with precipitation (Hanson et al., 2005; Letey, 1994). While utilizing NH4-based fertilizer instead of NO3-based fertilizer may help to retain N in the soil root zone a little bit longer, providing greater opportunity for crop uptake, NH4

+ is usually quickly nitrified in agricultural systems, often within one to three weeks (Robertson, 1997). However, research, some of it done in California, has shown that EEFs slow downward percolation of NO3

- under irrigated conditions. Stark et al. (1983) studied the effects of N fertilizer type and irrigation management on NO3

- movement on a loam soil. Less NO3- migrated below the root zone when

sulfur coated urea was used compared to conventional fertilizer product. However, water



management may swamp any benefits from EEF. Stark et al. (1983) found that excessive irrigation pushed NO3

- down through the soil profile irrespective of N source. Utility and likelihood of switching to EEF in California is questionable7, especially in the near term. To begin with, EEF are more expensive, with prices estimated to range from 9% (Snyder et al., 2009) to nearly double (California Nitrogen Assessment (CNA), stakeholder meetings) the prices of conventional synthetic fertilizers. This additional cost is often unwelcome to growers without clear yield increases. EEF in recent California vegetable crops trials raised yields only twice in nine experiments, 22% of the time (Hartz and Smith, 2009). In the late 1970s and mid-1980s, it was shown that nitrification inhibitors did increase N recovery in strawberry, cauliflower, and lettuce (Welch et al., 1985, 1979). Under current farming conditions, however, it is not clear if EEF will produce comparable benefits in California as in other regions where they are being promoted. Benefits of EEF are maximized when periodic and uncontrolled soil moisture decrease control of N, conditions only found during winter in some parts of California agricultural valleys. The more common production conditions–hot, dry, and fertigated–can provide equivalent or greater control of nutrients if managed astutely. Selecting appropriate fertilizer formulations to minimize emissions risk may be an important mitigation strategy for some losses. But there is no universal ‘best’ inorganic N source to serve growers needs and protect the environment.

7.1.2.3 Modify Fertilizer Placement and Timing

When fertilizer is positioned in the region of greatest root activity during periods of peak plant demand, plants generally have a competitive advantage over soil microorganisms. Resulting plant uptake reduces the soil mineral N pool, leaving less available for microbial transformations that prime it to be lost from the root zone. Ensuring N is available at the right place and time to satisfy plant demand while simultaneously minimizing inorganic soil N accumulation is a central tenet of sustainable N management (Roberts et al., 2007). When fertilizer is positioned in the region of greatest root activity during periods of peak plant demand, plants generally have a competitive advantage over soil microorganisms. Resulting plant uptake reduces the soil mineral N pool, leaving less available for microbial transformations, such as nitrification and denitrification, that prime it to

7 Strawberry is the only cropping systems where the use of slow release fertilizer is currently the industry standard (Strand, 2008; Reganold et al., 2010).



be lost from the root zone through leaching or atmospheric emissions. The capacity to achieve this synchronicity requires (1) knowledge of crop growth patterns and timing of nitrogen uptake, (2) ability to predict crop growth responses to changes in weather, and (3) the technology to precisely deliver N when and where it is needed. Information to satisfy the first requirement is reasonably available for field and vegetable crops, and is becoming increasingly available for tree crops (Saa et al., 2014; Muhammad et al., 2015). The second requirement is more difficult to meet for crops grown during the rainy season because of California’s highly variable weather from one year to the next, which can cause yields to vary by 50%. For irrigated crops, though, variability of precipitation does not play a large role. For example, fertigated systems are well suited to match N supply with crop N demand. Improving the timing and placement of fertilizer applications almost universally increases N recovery and often results in greater crop productivity. Scheduling fertilization events to coincide with periods of peak crop demand is critical to improve uptake and N use efficiency. For example, in avocado, specifically matching fertilization events with key phenological periods of rapid vegetative growth (mid-November and mid-April) increased productivity—total weight and fruit size–from 30% to 39% over four years (Lovatt, 2001). Avoiding using N fertilizer prior to winter is an equally important timing strategy. Fertilizer applied without actively growing plant cover is often lost. In a peach trial, fertilizer recovery increased 18% (58% vs 50%) by simply applying N in spring versus fall (Niederholzer et al., 2001). Even more dramatic results illustrating the need to avoid applying N in the fall are available from research throughout the Midwestern United States (Robertson et al., 2011; Snyder et al., 2009). Knowledge of crop growth patterns underlies the ability to split fertilizer applications to meet crop demand. Each crop species has distinct growth patterns, where nutrient demand is critical to further plant development. But generally, N demand of fruiting crops increases steadily while fruits develop and then declines in a bell shaped pattern over the season. In contrast, non-fruiting crops such as lettuce will increase gradually and require increasing amounts of N throughout the entire production cycle(Hartz et al., 1994a). Practical complications stem from the need to ensure sufficient quantities of N when peak N demand occurs, anywhere from a few weeks as in corn (Pang and Letey, 2000) to a few months as in pistachio (Rosecrance et al., 1998). It is important to note that improved application timing does not always result in increased productivity. Hutmacher et al. (2004) demonstrated that yields of Acala cotton grown across six farm sites in the San Joaquin Valley were statistically similar regardless of whether one or two applications were used. Resources required for additional application would thus have little value.



Fertilizer placement can also have a large impact on crop growth and N recovery. For example, Linquist et al. (2009) compare yields and fertilizer recovery of rice grown relying on surface or subsurface applications. Fields with only subsurface N applied recovered an average of 46% more N (53% vs 38%) and grain yields were higher, with the authors hypothesizing that surface-applied N was more susceptible to nitrification-denitrification losses, compared to sub-surface applied NH3. Placement can also affect N2O emissions. Several laboratory and field studies from locations outside of California have shown that concentrating fertilizer N, for example by applying it in bands, tends to produce greater N2O emissions than dispersing the fertilizer N, for example by broadcasting or disking (Engel et al., 2010; Tenuta and Beauchamp, 2000). These findings were confirmed by a Central Valley field study in corn production, that showed applying urea ammonium nitrate as two bands reduced cumulative N2O emissions by almost 70%, and applying one band to the shoulder of the planting bed reduced emissions by almost 60%, compared to applying one band directly on the bed (Burger and Waterhouse, 2016). One study (Hultgreen and Leduc, 2003 cited in Snyder et al., 2009) shows lower N2O emissions from band placement versus broadcast surface applied urea. Increased emissions from band placement might be attributed to extremely high N concentrations within the small area covered by the band; essentially, banding creates a hypersaturated zone. This is especially true for highly concentrated alkaline-forming N fertilizer materials, such as anhydrous ammonia, that have been shown to result in a build-up of nitrite, which then becomes the substrate for N2O production (Maharjan and Venterea, 2013). The potential for improved placement and timing of fertilizer N to significantly alter the current N fluxes from croplands on a statewide basis, however, ultimately depends on the extent of adoption of practices that result in greater N uptake efficiency, reduced NO3

- leaching, and lower N2O production potential. Some evidence suggests that some of these practices are already commonplace. For example, growers have been splitting fertilizer N applications for some time. The most recent statewide fertilizer use survey asked more than 800 growers in the late 1990s about their N management in 1986 and 1996 (Dillon et al., 1999). The number of respondents that applied N in a single application decreased by 9.2% (down to approximately 30% of respondents) and the number of growers that applied three or more applications rose 5.7%. Current use of these practices is largely not quantified. However, research has repeatedly demonstrated yield benefits from these practices and this aspect underlies most recommendations (Hartz et al., 1994b; Breschini and Hartz, 2002; Rosecrance et al., 1998; Lovatt, 2001).



There are clearly specific production systems where more attention to better timing and placement may be warranted. Rice may be one case where better placement would increase N recovery (see discussion above) and strawberry may be another case where research on the timing of N fertilizer application (currently largely applied approximately 6 weeks prior to planting) may need to be reevaluated, especially in light of changes in management due to restrictions on the use of methyl bromide. Precision agriculture technology8 may assist in improving fertilizer placement as well as in-season application timing for some field crops. Rice and cotton have been the focus of some experimentation with and adoption of precision agricultural technologies (Roel et al., 2000). Evidence of its application and effectiveness in the field is lacking. For many California cropping systems, technology is either unavailable (e. g., for horticultural systems) or not well adapted (e.g., not able to deliver nutrients at a meaningful scale of spatial variation). An effort is underway to adapt precision agriculture to tree crops; and harvesters and irrigation systems are under development (Rosa et al., 2011). Potential future fertilizer N efficiency gains from precision agriculture, beyond simple diagnostic soil and tissue tests, remain uncertain.

7.1.3 Water Management

Water regulates biological activity, chemical conversion of nitrogen (N), and physical transport of N in soils. Nitrogen moves into plant roots and tissues with water via diffusion and mass flow. Plants cannot assimilate N from dry soils and thus growth is, at minimum, compromised without the presence of sufficient water, and potentially altogether halted. Dry, well-aerated soils favor nitrifying bacteria, can be a source of NO, and tend to accumulate NO3

-, increasing the risk of leaching and denitrification losses when soils become rewetted. Excessive soil moisture, throughout the entire field or locally, physically dissolves and translocates soil chemicals including N. Saturated conditions also restrict gas diffusion. Soil environments with high water content reduce oxygen concentrations which stimulate denitrifying bacteria to use NO3

- in its place. Nitrous oxide production can result; the rate of which depends on local conditions, such as water filled pore space and the presence of a readily available energy source (e.g., C) (Davidson et al., 2000). Due to the significant influence of soil water content on a multitude of soil N cycling processes, any discussion of N management in agriculture must jointly consider water management.

8 Precision agriculture refers to a suite of technology-rich geospatial and information decision tools that increase spatial specificity of fertilizer N decisions (e.g., GPS, spatially variable fertigation).



Managing soil moisture content in California is unique by comparison to most other agricultural regions of the United States and elsewhere. The Mediterranean climate creates two distinct management periods: a summer growing season characterized by hot day time air temperatures and negligible precipitation and a winter cropping season characterized by cool moist weather with episodic and often intense rain events. The lack of summer precipitation, and the resulting dry soils, means crop production during these periods requires irrigation. Wetting and drying cycles resulting from irrigation generally reduce soil aeration, increase microbial activity, and accelerate the transformation of N. Although irrigation can create conditions conducive to N loss, irrigation by definition controls the quantity and timing of soil moisture, and thus provides opportunities to moderate the N cycle not found in rainfed systems. The prospects to control soil water content during winter cropping periods are limited (see Section 7.1.3.2). Large rain events that often occur during fallow and dormant periods between active growing cycles can be acute times of N losses when crop residues decompose and surplus mineral N fertilizer remains from the previous season (Cavero et al., 1999; Jackson, 2000; Kallenbach et al., 2010). A well-designed, -functioning, and -managed irrigation system maintains N in the root zone longer, increasing plant N uptake potential and reducing leaching losses (Feigin et al., 1982a, 1982b). The positive outcomes are mostly a consequence of the fact that water is the dominant factor dictating NO3

- movement laterally and vertically through the soil profile in the irrigated croplands of California. Collecting samples from tile drain effluent from 58 sites growing a range of crops throughout California’s agricultural valleys demonstrates that mass emissions of NO3

- (kg) are most significantly correlated with the amount of water moving beyond the root zone, even more so than the amount of N used (Letey et al., 1979; Pratt, 1984). Subsequent studies implicate poor irrigation efficiency, applying water in excess of beneficial uses (Feigin et al., 1982a, 1982b; Meyer and Marcum, 1998; Stark et al., 1983) and low distribution uniformity as culprits (Pang et al., 1997; Allaire-Leung et al., 2001) responsible for increasing drainage and leaching. Conclusions are thus consistent with that outlined in the seminal research of the 1970s (Pratt 1979 and subsequent publications): efficient irrigation is a prerequisite for high productivity, low leaching agricultural systems in California. The fact that soil water content significantly alters the nature and magnitude of gas emissions is well described (Schlesinger, 1999; Davidson et al., 2000). Yet data that relate irrigation management with control of gaseous emissions are limited. Presumably better water



management (e.g., higher efficiency and uniformity9) would decrease emissions due to enhanced control of wetting and drying cycles and dampening the effects of soil spatial heterogeneity, similar to its effects on leaching. Kallenbach et al. (2010) compared N2O emissions between furrow irrigated and subsurface drip irrigation in a processing tomato system and found that there were greater N2O fluxes from the furrow irrigated systems during the rainy season without a cover crop and during the growing season when a leguminous cover crop had been planted the previous winter. These results suggest the higher performing subsurface drip system (38.12 cm of water was applied versus 88.64 cm under furrow) provides mitigative benefits. However, research is needed to clarify the nature of the relationship, especially since many gas emissions represent only a small flux of soil mineral N (e.g., N2O ≈ 1.4% and NH3 ≈ 3% of N) applied in California (Krauter and Blake, 2009).

7.1.3.1 Improve Irrigation System Performance

Irrigation system performance is a function of underlying soil properties, technology, and management (Hanson, 1995; Breschini and Hartz, 2002). What that means, in practice, is that there are many factors that influence irrigation efficiency and distribution uniformity, some of which producers control and others which they do not. Growers have limited capacity to affect soil texture and heterogeneity (Childs et al., 1993; Letey et al., 1979). They, however, do decide when, where, and how much water to apply, subject to the constraints of the irrigation and cropping system designs, water and labor availability, and irrigation district policies. And it cannot be overstated that management decisions can override technical capacity of irrigation systems. Analyzing data from nearly 1000 irrigation systems, Hanson (1995) found that distribution uniformity and irrigation efficiency among irrigation types were similar in practice despite the greater technical potential of pressurized systems. It is likely that management has

9 Two interrelated metrics are used to describe irrigation system performance: uniformity and efficiency. Uniformity relates to the evenness of distribution of water applied or infiltrated across the field’s extent. No irrigation system can practically apply water at 100% uniformity. Spatial heterogeneity of soils and the length of the furrow affect uniformity. Because the common practice is to irrigate until the entire field receives sufficient water, non-uniform irrigations result in sections receiving significantly excess water. Length of furrows, differences between day and night irrigation set time, long irrigation set times, variable pressure, and clogged drip emitters are a few reasons for poor irrigation performance. Irrigation efficiency refers to the amount of water used for beneficial needs (crop evapotranspiration, leaching salts, frost protection, or cooling) related to the amount applied. The goal is to replace soil water lost through evapotranspiration. But low uniformity and the practicality of current systems including those reasons mentioned above and difficulty in predicting crop needs means that water often has to be applied at rates which exceed demand.



generally improved to capitalize on the advantage pressurized systems present in the 16 years since these data were collected, but that is not a foregone conclusion (e.g., Breschini and Hartz, 2002). Surface irrigation accounts for more than 50% of the irrigated acreage, although pressurized irrigation systems are increasingly widespread (Orang et al., 2008). Optimizing surface irrigation systems requires improving uniformity of infiltration and using the appropriate set times. The most effective way of increasing uniformity with surface irrigation is reducing the field length. Fields half the length (e.g., 150 vs 300 m) have been shown to increase uniformity by 10 -15% and to decrease subsurface drainage by 50% (Hanson, 1989). Such gains result from the shorter water advance times which reduce infiltration heterogeneity along the length of the field. Shorter furrows, however, frequently conflict with practices, including demand for labor, and represent a significant increase in cost for producers. Other options to increase performance with furrow irrigations are to surge irrigate (Hanson and Fulton, 1994) or to use torpedoes to compact soil and allow water to move more quickly down the furrow; the effectiveness of these practices depends on soil type (Schwankl and Frate, 2004). Pressurized irrigation systems provide a higher potential technical efficiency over surface applications. With pressured systems, improving irrigation is simple. The system must be designed, engineered, and operated correctly to achieve high performance standards. Switching from surface irrigation to a low volume irrigation system will improve performance, assuming appropriate management. In one study comparing irrigation technologies on lettuce in the Salinas Valley, similar yields were obtained with drip while only using an average of 61% of the water used on furrow over three years (Hanson et al., 1997). Goldhamer and Peterson (1984) found yields of cotton were greater with linear-move sprinklers than with furrow and produced less deep percolation. There is no doubt pressurized irrigation systems can distribute water more effectively if working properly and thus converting croplands to their use has significant potential to affect change of the N cascade. Decisions about the best strategy to improve irrigation management must consider the entire production envelope. The response is frequently dictated by farming and water economics. For example, in production of lower value crops, surface irrigation may be the only economically justifiable solution. Cotton is more profitable when using furrow irrigation, but this management practice presents greater potential for subsurface drainage (Hanson and Ayars, 2002), and thus the tradeoffs between economic viability and groundwater contamination are clear. Similarly, in some areas, parcel size and shape together with land ownership patterns preclude the viability of



sprinkler systems on forage crops. Change in these systems may require policies specifically designed to address these challenges.

7.1.3.2 Modify Subsurface Drainage

In areas of considerable soil drainage10, placement of engineered drainage systems is an option to decrease deep percolation of NO3

-. Drains change hydraulic soil properties creating a hydrologic gradient that moves water toward the drain, essentially creating a vacuum to suck up soil water. Captured leachate in agricultural areas is typically N-rich. Letey et al. (1977) found that median NO3

- concentration of tile drain effluent was 28 ppm NO3- -N, almost three times the legal

drinking water standard. By capturing leachate, drains prevent deep percolation of N to groundwater. Drainage presents potential for pollution swapping. Drainage simply transfers N concerns elsewhere. Removal of N from the soil decreases leaching potential, but also decreases denitrification potential (Lund et al., 1974). N in drain effluent still needs to be disposed of in an environmentally friendly way. Usually, drainage effluent is transferred off-site and disposed of into surface waters. N-rich effluent then becomes a source of surface water contamination and can contribute to indirect N2O emissions. Thus, drainage installation is not a stand-alone remedy for excessive N application. When used in combination with options capable of handling the N-rich wastewaters (e.g., biological denitrification reactors), installing drainage systems becomes an option that will reduce N loading.

7.1.4 Alternative Soil Management

Soil management, in the broadest sense, encompasses virtually every cropping decision a grower makes, from tillage to nitrogen (N) fertility management. Alternative soil management refers to a subset of practices to manage soil resources that are less widely adopted including: conservation tillage, organic N amendments, and cover crops. An important unifying characteristic of alternative soil management practices is that they add both C and N to soils either from plant or waste residues.

7.1.4.1 Conservation Tillage

10 Drainage refers to the movement and removal of subsurface water from the crop root zone. Well-drained soils create optimal conditions for crop growth and management. Excess water inhibits root development, contributes to root zone anoxia, promotes disease, and prevents access to fields by machinery for crop maintenance.



Tillage11 causes short and long-term changes in soil nutrient dynamics. Through exposing protected soil organic matter to microbial degradation and oxidation, tillage can lead to the loss of soil nutrients (Reicosky, 1997). For C, this means increased decomposition and CO2 respiration; for N, the result is growth of the soil mineral N pool and associated greater denitrification or leaching potentials. Because of this, some suggest that the intensity of tillage be reduced to attenuate negative perturbations of agricultural nutrient cycles (Lal, 2004; Pacala and Socolow, 2004). Conservation tillage12 presents its own challenges for managing nutrients. With slow decomposition of organic residues at the soil surface, net N immobilization can occur (Doane et al., 2009). Often this immobilization results in lower yields in the short term if not adequately accounted for in the fertility program (Doane et al., 2009). Microbial nitrification will decrease soil surface pH and presumably decrease volatilization potential, unless lime is applied. In the surface profile, reducing tillage intensity will increase soil organic C (SOC) in the topsoil (Lal, 2004). Evidence of increased SOC from conservation tillage throughout the soil profile is limited, despite widespread claims (Baker et al., 2007). Decaying organic residues form a readily available source of C for soil microorganisms, which can lead to increased rates of denitrification by comparison to conventional tillage (Li et al., 2005; Snyder et al., 2009). Though the effect is inconsistent, it appears to be sensitive to fertilizer placement (Venterea et al., 2011), and may be mitigated if reduced tillage is practiced in the long term (Six et al., 2004). Inconsistent experimental findings, interacting management factors, and antagonistic pollution potential suggest conservation tillage is an imperfect tool to manage N cycling in California. Conservation tillage is a technical term, with specific constraints on soil surface coverage, and simply reducing tillage intensity somewhat offers many agronomic and environmental co-benefits such as, dust control, water infiltration, and reduced fossil fuel consumption (Mitchell et

11 Tillage is the cultivation of land by ploughing, ripping, or turning soil. Tillage’s primary functions are to aerate the soil, control weeds, improve water infiltration, and distribute fertilizers throughout the profile (Loomis and Connor, 1992). Through tillage, soil structure, bulk density, and porosity as well as hydraulic properties such as water retention, hydraulic conductivity, water infiltration, and percolation generally improve (Balesdent et al., 2000; Wu et al., 1992; Lal, 1999; Hubbard et al., 1994). Tillage can also change soil pH, but direction of effects depends on the tillage regime (Blevins et al., 1983). An important consequence of tillage is that it increases C loss and soil organic matter decomposition. 12 There are many reduced tillage systems. The extreme is no-till where soils are not disturbed. Conservation tillage, which is more often practiced in California, relates to any tillage system that maintains at least 30% residue cover throughout the year (Mitchell, 2009).



al., 2007; B. A. Linquist et al., 2008). But its utility for sequestering soil C and mitigating N emissions from California croplands is questionable, especially in the near term. Root density and structure will have a large effect on soil C accumulation and crop growth patterns are sensitive to soil microclimates. Residue cover tends to decrease soil surface temperatures allowing roots to amass closer to the surface than they might otherwise. Comparisons of reduced and conservation tillage based only on measurements of surface soil C may therefore inherently bias results (Baker et al., 2007). Long-term observations at three sites demonstrate the potential variability in changes in C stocks. De Gryze et al. (2010) show changes in SOC range from -50% to 100% when comparing conservation with standard tillage. Net greenhouse gas emissions were slightly less from systems using conservation tillage. Kong et al. (2009) compared N2O emissions from minimum and standard tillage practices and found peak fluxes from minimum tillage using inorganic fertilizer were more than double that from standard tillage. Preliminary results from an ongoing examination of N2O emissions from tomato-wheat rotations under conventional and conservation tillage suggest reduced tillage emitted 37% less N2O of the N applied (48% versus 76%) (Kennedy, 2012). What can be concluded is that the mitigative impacts of reduced tillage depend on a series of other production factors which are difficult to predict and uncertain. Until recently, California cropping systems were not adapted for conservation tillage. Because reduced tillage requires specialized equipment and California crop typology is so diverse, a lack of appropriate implements impeded its use. Today, it is possible to grow processing tomatoes, cotton, rice, and lettuce under reduced tillage regimes (Madden et al., 2004; Mitchell et al., 2007; Venterea et al., 2005; B. Linquist et al., 2008; Doane et al., 2009). These four crops are cultivated on more than 600,000 ha, an area equal to roughly 20% of the cultivated irrigated farmland. Yet, the area cropped, while rising rapidly, using conservation tillage, was less than 1% in the mid-2000s (CTIC, 2004) suggesting a significant expansion potential. And it seems that potential is being capitalized on. More recent statistics indicate nearly 1 million acres of farmland are under conservation tillage in California (Warnert, 2012). Even though only a small fraction of croplands meet the precise requirements to be considered conservation tillage, expert accounts suggest producers throughout California appear to be reducing tillage intensity, especially in the San Joaquin Valley (D. Munk, personal communication). Based on the available data for California soils, climate, and crops, we conclude that the value of conservation tillage in mitigating N2O emissions specifically, or climate change more generally, is still speculative, with some conflicting results. Conservation tillage, however, is multifunctional and consideration of climate regulation in combination with other co-benefits warrants increased consideration of this practice.



7.1.4.2 Applying Organic Wastes

Applying organic waste products—manures, composts, and urban green wastes13—changes many features of the soil environment, largely for the better. Most importantly, these amendments add organic matter (SOM) to soils. Increased SOM improves aggregation and aggregate stability, which helps drainage, infiltration, and overall tilth—bulk density, porosity, and hydraulic conductivity (Wander et al., 1994; Rosen and Allan, 2007). Microbial biomass and labile pools of soil organic C and N also increase with organic amendments (Drinkwater et al., 1998; Poudel et al., 2001). Reserves of SOC and SOM serve as slow-release sources of nutrients and energy for plants and microbes, with the rate of availability depending on the material’s quality: C/N ratio, lignin, and polyphenol content (Palm et al., 2001). Use of organic wastes further promotes healthy and active soil microbial communities, slowing the pace of N turnover, minimizing the size of the soil mineral N pool, and in some cases mitigating N fluxes (L. Drinkwater et al., 1998; Reganold et al., 2010; Burger et al., 2005; Kramer and Gleixner, 2006). Efficient use of organic N in wastes is more complex than managing inorganic N mineral fertilizers. The first challenge is variability in the materials themselves. Organic amendments vary significantly in their N and C content. Differences are significant both between types of organic wastes (e.g., beef steer manure versus urban green waste) and within wastes derived from the same type of source (e.g., dairy manure). Of 31 samples of solid organic amendments intended for agricultural use in California, Hartz et al. (2000) found total N ranged between 10 to 47 g per kg among materials and the amount of organic N within the same material category ranged between 16 and 192% for materials with at least 3 samples. Large variation in N composition can be traced to source stock (e.g., animal diets or biomass) and conditions during processing. Without chemical analysis of waste prior to application, nutrient application rate cannot be estimated.

The second and related challenge has to do with the mineralization rate of N in organic wastes. As mentioned previously, mineralization occurs at variable rates subject to residue quality, environmental conditions (e.g., temperature and moisture), and management (e.g., tillage). These factors interact sufficiently to make SOM become plant available on time scales ranging from days to years, with accurate prediction of release rates requiring advance

13This discussion centers on manures and compost because of their overwhelming dominance of use (416 Gg of manure-N generated by animal production each year alone, nearly 2/5 of the N applied to croplands each year (Chapter 4)). In 2007, 258,122 ha of California cropland received manure (USDA, 2009). Similar concerns are applicable to biosolids.



computation and nontrivial data (e.g., Crohn, 2006). In an incubation experiment using California soils, between 4 and 35% of manure and composts were mineralized over the course of 10 months (Pratt and Castellanos, 1981). Growing seasons are often shorter in length and thus these results likely overestimate mineralization under typical production conditions. In four months, only an average of 11% of N was released for manures, 6% from composts containing manures, and 2% from composts composed of urban wastes (Hartz et al., 2000). To account for slow release, users of organic N end up having to apply rates well in excess of plant N demand, at least until soils reach an equilibrium where rates of mineralization equal N additions (Pratt, 1979; Pang and Letey, 2000). Although here we illustrate the issues with solid materials, similar concerns complicate the use of liquid manure, a common practice in Central Valley dairies (Feng et al., 2005). More homogenous, faster releasing materials are available (e.g., seabird guano, blood meal, and fish powder); however, cost limits their use in commercial settings (Hartz and Johnstone, 2006). Will using only organic N compromise productivity? This issue is very much debated (see Appendix 7.3). Some studies show yields are lower in organic than conventional systems (e.g., (Reganold et al., 2010; Jackson et al., 2004) when equivalent amounts of N are applied, presumably because much of the N contained within organic sources is not immediately plant available (Rosen and Allan, 2007). Others suggest yield differentials are rarely apparent (Badgley et al., 2007; Drinkwater et al., 1998; Reganold et al., 2001), or in some circumstances organic systems have even been shown to exceed the average yield of corresponding conventional systems in the same region (Bowles et al., 2015). The most recent meta-analysis suggests yields of cropping systems using organic versus inorganic materials were between 9 and 35% lower (Seufert et al., 2012), though many factors unrelated to fertilizer type may affect the productivity of the systems. Research results from California annual cropping systems demonstrate comparable yields can be achieved with intensive management. Over five years, yields of an organic rotation were similar to those from a conventional 2-year tomato-corn rotation (71 Mg per ha), both of which were slightly below average statewide yields over the same time frame (77 Mg per ha) (Poudel et al., 2002). Taken all together, it is generally accepted that present-day production systems using only organic N sources are less productive than those using inorganic sources, but notable exceptions exist in some crops and management systems. But is using organic N amendments more environmentally friendly than using conventional inorganic N sources? Conflicting results permeate the literature. Because applying organic wastes adds C and builds SOM, N tends to remain in the soil for a longer period. Drinkwater et al. (1998) suggests that the use of organic waste decreases leaching by nearly 50%.



One report demonstrates that by stimulating the active denitrifier community, N2 emissions increased in organic plots which leached 4.4 - 5.6 times less NO3

- than conventional plots (Kramer et al., 2006). Wang et al. (2008) show that 77% less NO3

- was leached from a rotation of cantaloupe and lettuce on a sandy soil using organic-N than one using synthetic fertilizer. It has been shown that N2O fluxes peak at greater levels in conventionally managed than in organic systems (Burger et al., 2005; Kong et al., 2009). In addition, Bowles et al. (2015) demonstrate that several organic processing tomato farms in Yolo County, California are able to achieve tight plant-soil N cycling, resulting in very low soil NO3

- pools and low potential of N loss, even while achieving crop yields equal to or exceeding the overall county-wide average. The authors attributed this phenomenon to a combination of efficient N management, high soil microbial activity, and rapid plant N uptake. On the other hand, simulations of N mineralization from poultry manure, corn uptake, and NO3

- leaching show that rates would have to exceed 600 kg of organic-N per ha to meet crop requirements; at this rate nearly 300 kg N per ha would be leached (Pang and Letey, 2000). Applying the same model to common liquid manure management practices (e.g., furrow irrigation with less than 80% uniformity), leaching rates approach or exceed 200 kg N per ha per year when N is applied at 1.4x plant uptake (Feng et al., 2005). Data that account for the difference in levels of N input and differences in levels of production suggest similar degrees of NO3

- leaching per unit applied and output from organic N and inorganic (Kirchmann and Bergström, 2001; Kirchmann et al., 2002). There is also little evidence that direct emissions of N2O from manures and composts differ significantly from synthetic fertilizers. Compilation of available data show that emissions from organic sources are approximately similar to, if not greater than, those from inorganic sources, 1-2% of N applied (Bouwman et al., 2002a, 2002b; IPCC, 2007). Use of organic wastes in California is constrained by logistical and health concerns. The economics of transporting bulky organic N containing materials limit the distribution of application. Liquid manure is moved at most about 3 or 4 miles from the place of production while solid materials are transported at most 50 miles, but often much less. More recently, concerns have been raised over the transfer of pathogens in manure. If the manure is not composted adequately, it can contain human pathogens (including E. coli H0157). Composting of manure emits much of the plant available N as gaseous emissions of N both reducing its fertilizer value and adding to regional air problems. Integrated fertility or low-input systems that utilize inorganic and organic N sources may achieve both production and environmental goals. Inorganic N fertilizer acts as a quick release



supplement to sustain crop growth until organic N mineralizes, more effectively synchronizing soil-crop nutrient cycles (Kramer et al., 2002). Incremental increases in yield and substantial decreases in emissions can result (Cavero et al., 1999; Poudel et al., 2002, 2001).

7.1.4.3 Biochar

Biochar is produced during the low temperature pyrolysis of organic residues (plant matter, animal waste) to generate renewable energy. The resulting material is then applied to land as a soil amendment. Although the use of biochar amendments to agricultural soils is receiving increased attention as a method for reducing N leakage while sequestering carbon, improving soil fertility, and increasing water retention in soil (Lehmann, 2007), little data are available to evaluate its ability to achieve the proposed benefits and even less to evaluate the mechanism by which it may do so. Use of biochar is impeded by the large variation in the materials. Materials sold, distributed, and applied under the “biochar” banner may differ significantly in their absorptive capacity and stabilization properties. Differences in materials arise from the wide variety of chemical composition of feedstock and conditions of pyrolysis. Variation further limits the capacity to predict or understand its interactions with soil processes. It remains to be seen if biochar is another “snake-oil” or if it truly has staying power.

7.1.5 Landscape Approaches

Not every action to control nitrogen (N) emissions must take place within field borders. Emissions, by definition, transfer N across boundaries between environmental systems. It is at the points where two ecosystems interface that landscape approaches change flux potential. Practices implemented at the field boundary or strategically distributed across the landscape can capture, recycle, and transform N prior to its release into the wider environment. Currently, most landscape approaches for N management aim to limit NO3

- movement from the biosphere to the hydrosphere by sequestration and denitrification.

Managing reactive N at the landscape scale offers a prospect for N control but adds concerns as well. When landscape features serve as sinks for N, sustainable reduction must result in long-term storage of N in the burial of plant materials and sediments. Without storage, impacts are delayed, not mitigated. Soil water and N content in the system is high and thus there is a likelihood of denitrification and N2O evolution. Unmanaged wetlands generally emit only a small quantity of N2O (Groffman et al., 1998). But it is once systems are overloaded with NO3

-



from agriculture that they become a substantive source of the greenhouse gas. Use may therefore cause pollution swapping to a limited extent, if denitrification conditions cannot be controlled.

Landscape approaches can be divided into two main categories. The first involves the management of natural vegetation at the field’s edge or stream bank. The second comprises engineering solutions. While it seems self-evident, it is worth noting here that the effectiveness of any landscape approach, natural or man-made, to regulate N cycling will depend on its positioning and size. A large, poorly sited landscape feature, outside a N flow path, will not interact with sufficient N to make a marked difference. Conversely, biological processes may be overwhelmed if the feature’s area is insufficient to treat the influent N load. This reality means features often have to be located on prime farmland, creating additional opportunity and operations costs.

7.1.5.1 Manage Natural Vegetation

Vegetative areas at field boundaries, which can range from simple grass buffer strips to complex multi-strata riparian ecosystems, reduce NO3

- loading to the environment. Grasses, herbaceous perennials, and trees typically intercept NO3

- as it moves across the soil surface with sediment and runoff or with their roots during subsurface transport. A meta-analysis of vegetative buffers indicates that the median reduction of NO3

- was 68.3% but actual reductions varied widely, from 2.2 - 99.9% (Zhang et al., 2010). Variation in buffer performance can be attributed to its size and topographic positioning. Accordingly, larger buffers sequester more NO3

-, up to 88% of influent at 30 m. Isotopic N experiments indicate actively growing plant cover is important to maintain and increase buffer capacity, with 2/3 greater NO3

- uptake when vegetative buffers were managed by cutting than is taken up in unmanaged systems (Bedard-Haughn et al., 2005, 2004). Riparian areas at the edge of waterbodies reduce NO3

- to similar degrees. Data from 89 studies on 45 riparian areas indicate an average 67.5% N removal rate (Mayer et al., 2007). Riparian zones appear to be more effective at removing subsurface NO3

- than surface runoff suggesting that aggregate effects of soil type, subsurface hydrology, and denitrification potential may have a large influence on their utility as a N management measure.

Dedicating land for vegetative areas can have its downside though. In particular, it removes land from production, with concordant economic consequences. Vegetative areas may place greater demands on labor because of the need to manage the features, be it mowing or biomass harvesting. In some cases, buffers may increase weed or pest establishment (although, conversely, they may also provide habitat and food sources for pollinators and other beneficial



organisms). Thus, vegetative buffers may present tradeoffs with economics, labor, and agricultural chemical use.

7.1.5.2 Construct Engineered Solutions14

Human engineered systems, such as constructed wetlands and denitrification reactors, are designed to process N in influent in much the same way as natural features, relying on processes of uptake and/or denitrification. Their ability to reduce N load of effluent and protect water quality is determined by a large number of site-specific factors, such as the timing, magnitude, and concentrations of nutrient load, and hydrologic properties, such as residence time, and thus high variability in efficacy should be expected (Iovanna et al., 2008). Nevertheless, constructed wetlands and denitrification reactors appear to be effective. In California, O’Geen et al. (2007) studied a 1-year-old wetland and a 10-year-old mature wetland in the San Joaquin Valley. The newly constructed wetland removed an average of 22% of NO3

- while the more mature wetland removed 45% (O’Geen et al., 2007). Irrigating pasture tends to produce artificially occurring wetlands in drainage basins. Even at low residence times (less than 2 hours), wetlands in these circumstances are capable of reducing NO3

- loads by 60% and total N by 40% (Knox et al., 2008). Recently, development and deployment of “denitrification reactors” has been proposed to

reduce the N loading from agricultural runoff, as well waste- and stormwater (Collins et al., 2010). A denitrification reactor is essentially a trench with high C infill, such as wood chips. Nitrate-rich waters transit through the C rich substrate slowly enough for denitrification to take place. Management is key to ensure appropriate denitrification conditions are maintained and remains the largest concern. If operated with low residence times, too high N concentrations, or limited C, denitrification reactors may become a source of N2O. Substrate must be high in carbon and resistant to decomposition so that denitrification is not limited and the material does not have to be replaced often. As with other landscape approaches, the effectiveness of denitrification reactors to reduce the N in the effluent load can vary based on the C material, residence time, and influent N concentrations(Collins et al., 2010; Schipper et al., 201015).

Only a few large-scale bioreactors are in operation in the United States, principally distributed at commercial drinking and treatment facilities (Jensen et al., 2012). Bioreactors are an effective technology reducing loading at a smaller scale. Robertson and Cherry (1995) show

14 Many technologies applicable to agriculture were either developed or are also used for treatment of water from wastewater treatment plants and stormwater. 15 See Ecological Engineering (2010) volume 36, issue 11 for special issue on bioreactors.



that bioreactors can treat leachate from 60 ppm to 2 -25 ppm NO3-, a removal of 74 – 90%.

Recently, they have been shown to be effective for treating effluent from onsite wastewater treatment systems (Leverenz et al., 2010). The technology could also be effective for treating agricultural leachate and runoff from tile drains because runoff N is already in the form of NO3

- and therefore does not need to be nitrified prior to denitrification, as is the case in industrial wastewater treatment. Effluent from field drains at a local scale or aggregated at larger scales may prove to be an option worth exploring.

7.1.6 Agrobiodiversity

Biodiversity, and agrobiodiversity16 more specifically, improves nitrogen (N) cycling through altering the pace of N turnover, stabilizing soil N within organic matter, extracting a greater fraction of mineral N from the soil, retaining N in the landscape, and reducing the exchange of N between adjoining ecosystems or among land, air, and water (Brussaard et al., 2007; Smukler et al., 2010; Young-Mathews et al., 2010). It achieves all this through virtually every plausible N control mechanism, from efficiency to transformation. Managing for diverse agricultural landscapes, therefore, holds some promise for addressing N concerns in California agriculture. However, significant technical and financial obstacles impede diversifying production systems and their surroundings within their current geometry and technological, institutional, and regulatory envelope.

7.1.6.1 Plant Green Manures and Trap Crops

Cover crops are plants grown for reasons other than to generate income, with altering soil N cycling being one of the most frequent goals. Cover crops can be grown concurrently with a cash crop, as when they are planted between rows in perennial systems, or between annual crops when fields would otherwise be fallow. In either circumstance, cover crops influence N cycling by changing soil physical and chemical properties after they are incorporated into the soil. Effects ranging from rapid N mineralization and availability to near complete inorganic N immobilization are possible, with the consequences being a function of characteristic traits of the cover crops species (biomass, C/N ratio, N fixation) and environmental conditions of production (length of growing season, temperature, soil moisture) (L. E. Drinkwater et al., 1998; Hu et al.,

16 Agrobiodiversity refers to domesticated and non-domesticated species that support food provisioning. This clearly includes plants and animals that are consumed but also pollinators and soil biota that are necessary for production.



1997; Carol Shennan, 1992). Variation in the potential N cycling impacts and the diverse set of cover crop species and cash crop production systems places a premium on thoughtful species selection when using cover crops. When planted for N utility, cover crops serve one of two opposing objectives and it is important to differentiate between them. Leguminous cover crops add new N to the soil (e.g., green manures) while non-leguminous cover crops (e.g., trap crops) capture and recycle N back to the soil surface. Green manures are grown to increase the soil N pool in support of cash crop nutrient demand (Jackson, 2000; Patrick et al., 2004). Incorporation and decomposition of cover crops material provide soil microbial communities energy to mineralize N contained within the green manure. Cover crops with a low C/N ratio (i.e. <20) ensure rapid decomposition and avoid net microbial immobilization of soil N which would have a potentially deleterious effect on cash crop growth (Wyland et al., 1995). The quantity of N made available is determined by the rate of fixation and biomass production, both controlled by inherent species traits as well as environmental conditions and length of crop cycle. Shennan (1992) reviewed cover crops for California and found that reported rates of fixation ranged from 56 to greater than 200 kg N per ha. Fixation rates at the higher end of that range are at levels sufficient to meet the nutrient demands of most crops. However, as with inorganic N, uptake efficiency of legume N is generally low—averages about 30% (Crews and Peoples, 2005). Part of the inefficiency results from rapid mineralization of N after incorporation, which potentially decreases N supply and synchrony with crop demand. In a California no-till processing tomato system, Herrero et al. (2001) found that soil mineral N was higher in systems following cover crop incorporation than in systems using application of inorganic mineral fertilizer, demonstrating the potential for poor synchronization. As previously discussed, nutrient supply and demand asynchrony increases the risk of leaching and gaseous emissions, although higher emissions do not always result. Crews and Peoples (2005) suggest that legume N in irrigated production may decrease N loss in part because of a greater incorporation of legume N into SOM. By comparison to inorganic N sources, direct N2O emissions from leguminous N sources are often reported to be lower, approximately ½ on average (Rochette and Janzen, 2005). Despite the potential drawbacks, a meta-analysis of research on replacing fallows with leguminous crops found that yields were only an average of 10% less when using legume cover crop to support cash crop growth instead of inorganic fertilizers (Tonitto et al., 2006). These results suggest the potential to partially substitute organic N sourced from cover crops for inorganic N. Non-leguminous cover crops are used as trap crops to capture inorganic N remaining in the soil following cash crop production. This is important because without actively growing plant



cover (e.g., in winter fallow and dormant periods) soil N builds up due to mineralization of plant residues and is particularly vulnerable to loss (Jackson et al., 1994). With the EPIC biogeochemical model, research predicts that leaching of NO3

- in tomato and lettuce systems can exceed 150 kg per ha following the primary summer production season (Cavero et al., 1999; Jackson, 2000). Using cover crops over this period consistently and significantly reduces the size of the NO3

- pool and pollution potential (Jackson et al., 2003). By capturing and sequestering what would have been lost, trap crops minimize the inorganic N pool and present an opportunity to recycle N into the cropping system upon their decomposition. Crop growth patterns and root density and structure determine a species’ ability to extract N from the soil. Because of the differences between crops, strategically designing cropping systems and crop rotations is necessary to achieve a high system N efficiency. Cover crops offer non-N related benefits as well, such as addition of organic matter, disease suppression, erosion control, and maintenance of beneficial insect population, and these co-benefits may drive their use (Ingels et al., 1994). Utilization of cover crops to achieve N cycling objectives in California faces many challenges, however. Most frequently cited challenges include short time frames between cash crops limiting total cover crop biomass production, depletion of soil water reserves by the cover crop, and costs of establishment and incorporation (Jackson et al., 2003). In addition, cover crops in orchards and vineyards can change the microclimate, which may lead to frost damage to perennial crops (Ingels et al., 1994). Because of the physiological differences between crops, pairing the appropriate cover crop with the cropping goal is essential to maximize benefit (Ingels et al., 1994). The growth habit, flowering period, maturity, and reliability of self-reseeding are a few of the characteristics that are important to consider when selecting the right cover crop. Cover crops grown in annual systems, for instance, may need to be fast growing species to maximize biomass production and N uptake during the short windows between cash crops. In perennial systems cover crops that are strong self-reseeders may become invasive weeds competing for light and soil resources. Ultimately, successful use of cover crops requires evaluating the benefits and potential concerns of a cover crop species within the context of a specific farming system. 7.1.6.2 Diversify Crop Rotation

Impacts of diversifying crop rotations on N cycling will depend on rotation used, the species substituted, and the management of the crops. It is essential to consider entire cropping system N efficiency. For example, safflower is regularly fertilized with 110 to 170 kg N per ha but it has been shown to produce high yields with minimal addition of N fertilizer relying extensively on



residual N in rotation with other crops (Kaffka and Kearney, 1999; Bassil et al., 2002). Diversifying rotation to include safflower will only be beneficial if the entire rotation is accounted for. Unfortunately, crops with significant extractive capacity tend to be of low economic value. With the high costs of land and water in California, the inclusion of such crops is often untenable. One unique case is when using alfalfa in rotations. Alfalfa is a legume that fixes atmospheric N, arresting the need for synthetic N inputs. Unless an ‘N credit’ is given for N released from decaying alfalfa residues when it is plowed under, the subsequent crop may be over-fertilized (Robbins and Carter, 1980). Recent research has shown that an appropriate credit ranges from approximately 67 kgs N/ha to 145 kgs N/ha depending upon soil type, age and status of alfalfa stand, weed intrusion, degree of foliage plow-down, time of year, and time elapsing between plow-down and the subsequent crop planting (Putnam and Lin, 2016) . A diverse array of crop rotations is used in annual croplands of California. Some patterns are widespread (e.g., processing tomato-wheat in the San Joaquin Valley; lettuce-lettuce-cole in the Salinas Valley), while others are much less common. Ongoing research documenting rotations in Kern County shows that the 10 most commonly observed rotations account for 48% of cropping patterns (MacEwan and Howitt, personal communication). These data illustrate that while clear patterns are discernable, there is a substantial variation. Deviations in planting decisions are consequences of external drivers, such as market, weather conditions, and availability of water, as well as internal drivers such as relative costs of production. Current conditions are a good example. High commodity prices are leading to a resurgence of cotton production in the San Joaquin Valley after years of decline since 2005, likely displacing area previously converted or planned for other crops. On the other hand, low commodity prices for milk require that dairies produce low-cost forage crops to minimize feed expenses, a situation which may limit the diversity of forage crops they can choose from. 7.1.6.3 Enhance Soil Biological Activity and Diversity

Soil animal and microbial diversity is part of the biological resources of agroecosystems and thus managing their activities should be considered as part of the N management portfolio. Soil bacteria determine the pace of N cycling where most N transformation processes are direct results of the activity of these microorganisms including denitrification, nitrification, immobilization, and fixation. Through these processes, soil fauna affect the rate of N reactions, effectively manipulating the size and duration of soil N pools (Drinkwater et al., 1995). In addition to the effects on chemical composition, soil organisms affect physical composition and



structure of soils, which changes gas diffusion and hydraulic properties. At the same time, soil biota is affected by N availability. When soils are low in available N, fungal communities dominate. In contrast, bacterial communities tend to dominate soils with significant quantities of N available. Management decisions can influence soil biodiversity directly or indirectly. Yet few approaches aim to directly manipulate soil biodiversity and behavior. Corkidi et al. (2011) demonstrate the potential value of such approaches. The authors analyzed leachate from containers growing three common nursery crops and found that the NO3

- and NH4 concentration of that leachate from pots inoculated with arbuscular mycorrhizae was up to 80% lower. Alfalfa producers directly enhance soil microorganisms as well. Prior to planting a new stand of alfalfa, soils are often inoculated with Rhizobium to promote symbiotic N-fixation. More often, however, soil communities are managed by the indirect means of modifying their environment. Management practices, as discussed above, will each have an effect on the chemical properties of the soil environment, such as pH, oxygen, N, and C availability. Changing conditions has the capacity to change microorganism diversity, with substantial effect on C stabilization and N cycling (Six et al., 2006; Brussaard et al., 2007). Whilst the functions of soil biodiversity are beginning to come into focus (e.g., Wardle et al., 2004), there are not many mechanisms to translate that knowledge into practical applications for today’s current agricultural systems (with at least one exception – use of arbuscular mycorrhizae in plant phosphorus acquisition, Smith et al., 2011. Development and implementation of this approach requires new research into the functional and technical aspects of how it would be implemented in the field. Thus, it is unlikely to be a significant factor in helping California better manage N use or reduce saturation in the immediate future. However, active management of microorganisms is the foundation of N treatment in other sectors (e.g., wastewater treatment). A first step would be to identify the plausible opportunities that could work at the field scale.

7.1.7 Genetic Improvement

7.1.7.1 Improve Crop Genetic Material

Nitrogen use efficiency in plants is a function of the efficiency of uptake (recovery efficiency) and the efficiency of utilization (physiological efficiency). Genetic traits determine a species’ nitrogen (N) demand, ability to recover soil N, and how well it utilizes N once assimilated. Not until recently has N use efficiency become a subject of interest for plant breeders. Previously, other



desirable traits were the objects of selection (e.g., disease resistance, yield, or product quality). The consequence has been, in some cases, an inadvertent selection against N use efficiency. For example, a plant’s ability to explore the soil and take up N is determined by its root system architecture. The root architecture depends on the species but significant intra-specific variation of rooting depth, density, and branching has been documented (de Dorlodot et al., 2007). Commercial lettuce cultivars maximize development of the head or shoot, at the expense of a vigorous root system. The small root system restricts the plant’s ability to excavate N and water (Burns, 1991). Producers, in turn, must manage N for a crop that requires N in very significant quantities with a root system smaller than the size of a football by timing inputs, a near impossible task. Notice of the agricultural N-related resources degradation has prompted new research aimed to genetically maximize N use efficiency (NUE) (Hirel et al., 2007). Genetic improvement of crop plants may contribute significantly to addressing N concerns in California croplands in the short to medium term, less than 20 years.. Recently, application of molecular tools has contributed to the more complete understanding of many underlying processes such as N transport, enzymatic reaction, and function (Good et al., 2004). Although mechanisms of internal plant N utilization and recycling have been better described recently, rarely has genetic improvement produced greater yields with less N. Genotype by environment interactions are common, which demonstrates significant plasticity of the trait, making experimental selection challenging (Hirel et al., 2007). Phenotypic plasticity underscores the challenge in selecting for high NUE and partly inhibits the translation of results from controlled experiments to field conditions (Hirel and Lemaire, 2006). Future gains in crop NUE due to genetic improvement will require experiments that span agronomy, physiology, and molecular genetics. Nonetheless, the principle reason we believe that genetic manipulation can yield results for California soon is that the majority of genetic NUE research centers on field crops (rice, wheat, canola, or corn) or model species such as Arabadopsis or Nicotiana. Lessons learned from these systems may eventually benefit California producers of those commodities; approximately 800,000 ha or 38% of the cropland, which do have a large impact on groundwater NO3

- contamination. But still greater emphasis examining NUE in vegetables and trees is needed for the effect of genetic improvement to include the bulk of future cropped area.

7.1.7.2 Breed Animals for High Feed Conversion Efficiency

Feed conversion is the amount of feed required to produce one unit of product (i.e. eggs, meat, wool, or milk). As feed conversion efficiency improves, less feed is required per unit output. This



translates into a reduced need for farmland to grow feed inputs as well as reduced nutrient excretion (manure). Genetic improvement provides one way to improve feed conversion on livestock and poultry farms.

Genetic improvement of farm animals has historically improved feed conversion, produced higher yields more rapidly, and resulted in less manure generated. The most significant advances have perhaps come in broiler breeding. Comparison of the Athens-Canadian random bred control (ACRBC), a common breed from the late 1950s, and the Ross 28 broiler, a current breed, provides evidence of the potential benefits (Havenstein et al., 2003a, 2003b; Cheema et al., 2003). The Ross 308 broiler on the 2001 feedstuffs was estimated to have reached 1,815 g body weight at 32 d of age, whereas the ACRBC on the 1957 feed would not have reached that body weight until 101 d of age. The shorter age to market resulting from improved feed conversion would require far less feed input (and associated land to grow the feed) to achieve similar product and have markedly less manure output. Comparisons of carcass weights of the Ross 308 on the 2001 diet versus the ACRBC on the 1957 diet showed they were 6.0, 5.9, 5.2, and 4.6 times heavier than the ACRBC at 43, 57, 71, and 85 d of age, respectively. The authors attributed 85% of the improvement to better feed conversion. However, improved performance has come at a cost. Concordant to increased growth rates, there has been a decrease in the adaptive immune responses (Cheema et al., 2003).

Dairy production has also benefited from genetic improvement of animals. By one estimate, 57% of the increase in milk yield between 1957 and 1997 in the United States was the result of better genetics (Cassell, 2001). Nation-wide genetic improvement has led to fewer dairy cows, less feed, and less manure while supporting the demand for dairy products (Capper et al., 2008).

The potential for genetic improvement to yield additional benefits for managing N in animal production is not significant in the short term. 7.1.8 Animal Nutrition and Feed Management

Protein nutrition influences productivity, profitability, and the efficiency of nitrogen (N) use in cattle and poultry production systems. Production of milk, meat, and eggs are correlated with crude protein intake (Bailey et al., 2008; Kebreab et al., 2001; Sterling et al., 2002). It is important to supply protein in sufficient quantities to support growth and development. When diets are formulated for specific protein and amino acid requirements, bioavailability of N and assimilation improve (Powell et al., 2010; VandeHaar and St-Pierre, 2006; Huhtanen and Hristov, 2009; Nahm, 2002). Consequently, an increase in resource use efficiency takes place.



Feed utilization efficiency has multiplicative impacts on N cycling within the animal production unit and croplands. The amount and form of N excretion is influenced by the type and degradability of protein and energy source in the diet. For example, increasing the energy concentration of the diet and using low degradable starch sources, such as corn in concentrates, could reduce not only the total amount of N in excreta but also the proportion of N in urine (Kebreab et al., 2002), which in turn reduces ammonia emissions. Feed utilization efficiency also decreases the total demand for animal feeds (assuming livestock production remains constant). Coincidentally, N emissions from feed production and transportation are reduced. At the same time, less N excretion takes place, reducing the disposal/recycling burden on land and emissions. Meyer and Robinson (2007) provide an illustration of the benefits of feed management on manure handling. The authors inventoried feedstuffs and feed management at seven dairies in California and found that dairies operated at between 16 and 27% N utilization efficiency. That means that for every 1,000 kg of N fed, the least efficient dairy excreted 840 kg of N, while the most efficient dairy excreted only 730 kg of N. The consequence is that the less efficient dairies require 15% more land for N application or that the more efficient dairy could milk 15% more cows with the same amount of land assuming the same application rate and efficacy of organic N use. Even if manure handling practices remain the same, less N excretion could potentially reduce emissions because most emissions are in part related to the amount of N excreted. With more than 2.4 million cattle and 350 million birds on feed year-round and up to 2.6 million cattle on supplemental feed in California, feed management presents considerable potential for reducing direct and indirect N emissions due to California’s animal feeding operations. But the magnitudes of the benefits are hard to characterize because few data are



available to evaluate animal feeding practices in California. Because of this, the discussion here will be restricted to cattle. Castillo et al. (2005) surveyed feed management practices on 51 randomly selected dairy operations in Merced county and found crude protein contents of lactating cow diets averaged 17% ± 1.19 (SD). This finding suggests that the average operation is not overfeeding N (the National Research Council (2001) recommendation for crude protein consumption in lactating dairy cows is 16.5%). Precision feeding of N is the matching of crude protein with physiological requirements. This survey by Castillo et al. (2005) demonstrates that the dairies feeding more than one diet had higher N utilization and dairies feeding three and four diets had statistically significantly higher N utilization than those feeding uniformly (Figure 7.1.1). However, feed management rarely accounts for the differential requirements of animals during various points in their life cycle well. Calves, dry, and lactating cows demand a different amount of crude protein. If fed the same diets, only altering dry matter intake, overfeeding of N results in increased N excretion. Recognition of the variable needs of cattle has led to calls to

increase staged or precision feeding (Meyer and Robinson, 2007). Most animal operations formulate diets to provide minimum required nutrient concentrations at the lowest cost. Because protein is among the most expensive ingredients, their use is generally tightly monitored. Despite close attention, N is sometimes fed in quantities larger than is required to meet physiological demand. This is especially problematic with low-cost by-product feeds which are often of variable composition (DePeters et al., 2000). An increasingly important concern is the use of distiller’s grains as a feed. Distiller’s grains are a by-product of ethanol production and are commonly fed to cattle because of their low cost and high nutrient concentration, which tends to

FIGURE 7.1.1 Nitrogen Utilization Efficiency of 51 Dairies in Modesto. Source: Castillo et al. 2005.



be two to three times as high as unprocessed grains (Belyea et al., 2004). Without reformulation, diets quickly exceed N assimilatory capacity of the animals and excess N is excreted. Hao et al. (2009) shows that NH4 composition of manure increases with increased consumption of distiller’s grain. Given the relatively low cost of distiller’s grains, however, reformulating diets by substituting other materials could potentially raise the overall costs of feed. Feed management includes the use of dietary additives to enhance production. The additives may be yeasts, enzymes, microbials, ionophores, or proprietary materials. Some additives are well researched, and their mode of action is well defined. Other additives have undergone less rigorous research and little is known of their efficacy in the animal or their subsequent impact on the environment. The most widely researched and publicized supplement is rBST. Some evidence indicates that this hormone decreases the protein requirements for maintenance and lactation by 3.2% and N excretion by 9.1% per kg of milk production (Capper et al., 2008). However, consumers have raised concerns over its use and subsequent transmission into the food supply. Less than 10% of the milk produced in California uses rBST and its future use is expected to continue to decline (D. Meyer, personal communication). Additives and supplements have been important in reducing the environmental impact of poultry production. Gains in NUE are the consequence of widespread feeding supplementation. Addition of amino acids and growth promoting substances resulted in reduced N excretion between 5 to 35% in poultry depending on the feeding strategy (Nahm, 2002). When considering feed management/NUE of California animals, it is important to remember the role of animals in the broader agricultural system of the state and how crop diversity affects diet formulation. California cattle and dairy cows, in particular, serve an essential recycling function. A significant fraction of their diets can be derived from consumption of agricultural by-products, with variable and often less known N concentration. In this way, they concentrate and consolidate N from agricultural industries throughout the state (DePeters et al., 2000). Without them, a significant amount of N would have to be handled, processed, and disposed of by other means. Furthermore, ethanol production creates access to a cheap protein (N) source, distiller’s grains. Use of this feedstuff complicates diet formulation due to the near double N content compared to unprocessed grains, increasing excretion and emissions (Hao et al., 2009, Chapter 7).

7.1.9 Manure Management Manure management typically refers to the practices used to handle animal waste following excretion. In fact, planning for manure nutrient recycling and disposal should begin prior to



excretion, with protein management. But here, we restrict the discussion to the methods for handling manure nitrogen (N) itself and discuss it within the context of manure management trains—collection, storage, treatment, and land application17. Understanding the process underlying the individual component practices is important; however, manure handling requires sets of practices to conserve manure N for land application and thus, in practice, a whole farm approach is necessary if emissions are to be controlled (Castillo, 2009; Powell et al., 2010). It is precisely because of this reason that practices that do not necessarily change N characteristics but do enable greater management capacity of manure N, such as liquid-solid separation, are discussed.

7.1.9.1 Collect Manure More Frequently

Manure collection in animal feeding operations aggregate N for storage, treatment, and later application to crop fields. Collecting manure more frequently after it is deposited in barns and open lots will almost certainly decrease N emissions, although data are generally insufficient to quantify the extent. Reductions result from moving the fecal and urinary N from a location with an environment amenable for NH3 volatilization to one where chemical and physical processes are more easily manipulated to create less hospitable conditions. Frequent flushing in freestall barns transfers the highly volatile urinary N into anaerobic conditions (lagoons) where pond pH and depth determine volatilization rates (Mukhtar et al., 2009). Since dairy operators flush freestalls with recycled lagoon water (rich in NH4), increased flushing frequency may cause a marginal amount of additional volatilization. The increase is likely negligible and far outweighed by removing the manure more rapidly from the barn surface. Frequent removal of manure helps control emissions from solid manure too. Corrals, open lots, and poultry houses are vulnerable to volatile, and somewhat susceptible to leaching, losses because of the high rates of N excretion, concentrated spatial distribution of urine and feces, and constant mixing of the soil surface by animal movement (Chang et al., 1973; Hristov et al., 2011; Xin et al., 2011). Frequent removal to longer-term storage and treatment processes (i.e. composting or dying) decreases the emissions from housing areas; however, the larger N load transported into other components means there is an elevated risk of emissions from these farm components (Rotz, 2004).

17 This discussion draws heavily on the recent stakeholder process, “An Assessment of Technologies for Management and Treatment of Dairy Manure in California’s San Joaquin Valley” and we recommend this publication as further reading for those interested in these issues (TFASP, 2005). Additional discussion on land application of manures can be found in the section on using organic wastes.



Economic, operational, and regulatory considerations constrain the frequency of manure collection in California. Manure is bulky and heavy. Moving it, even over short distances, represents a significant undertaking. More regular collection will increase demand for labor, fuel, and machine time decreasing net profits. Even if the costs were not limiting, infrastructure restricts the rate of manure collection at many animal feeding operations. Storage and treatment facilities (e.g., lagoons, solid-liquid separators, drying pads) have a finite capacity and often operate near their limits. Structural expansion may be necessary to accommodate additional volume due to greater collection regimes. Economic and operational concerns aside, current and impending regulations for N and other pollutants dictate collection practices that may be complementary or antagonistic for N control. For example, dairy farmers in the Central Valley are already required to collect manure one to four times daily to control volatile organic compounds (VOC) (Stackhouse et al., 2011). The effect of more frequent manure collection on NH3 volatilization is unknown, but the potential tradeoffs or synergies illustrate the need to consider multiple pollutants jointly. In spite of the potential downstream emissions pressure and the functional challenges, more frequent collection would likely have net benefits for environmental N pollution. At this time, it is impossible to know the magnitude of the impact for the environment or for farming practices and economics. 7.1.9.2 Nitrification Inhibitors

Use of nitrification and urease inhibitors to control gas emissions has received increased attention recently (see discussion on enhanced efficiency fertilizers above). The chemical compounds that arrest or retard N transformations in soil have been tested on feedlots and in poultry houses. In both situations, urease inhibitors have proven effective to reduce NH3 emissions. Parker et al. (2005) applied it in beef feedlots and documented 49% to 69% reductions in NH3 depending on the rate of application. But the relative efficacy is temporary, lasting only 7 to 14 days in one study (Singh et al., 2009). Nitrification inhibitors can also reduce N2O emissions from both fertilizers and manure (Akiyama et al., 2010; Dittert et al., 2001). Akiyama et al. (2010) report that nitrification inhibitors reduce N2O emissions from N fertilizer by an average of 38% across a wide range of inhibitor chemicals, N sources, and land use types. Likewise 3,4-dimethylpryazole phosphate reduced N2O following manure slurry applications by 32% (Dittert et al., 2001). Use of nitrification inhibitors in manure management systems of California is extremely limited, likely due to cost and climate. However, there is no research on when, where and how they might be effective for California producers.



7.1.9.3 Separate Solids from Liquids

Solid-liquid separation systems are designed to divide manure by the phase of the material. The purpose is to segregate the manure into more homogenous components, in both form and constituency. Handling and treatment of individual fractions can then be specifically tailored for its composition and characteristics more easily. Liquids can be transferred more readily through the system without clogging pumps and pipes. Solids can be scraped, composted, applied as bedding, and potentially manifested off-site. Because the form of the N in the solid and liquid fractions of manure differs, with solids containing mostly organic N which is bound to C and more stable in the environment and liquids containing mostly urea and NH4 which is highly reactive and vulnerable to volatilization, operators can take advantage of nutrient value and control future N dynamics more readily. In short, separation enhances manageability. Multiple factors affect division of the solid from the liquid fraction. Inherent system properties, such as flow rate, characteristics of manure, particle size and nutrient load, influence the relative distribution of N in effluent and solids (Zhang and Westerman, 1997) . Meyer et al. (2004) evaluated the efficiency of a “weeping-wall” separation system in California and found no significant reduction in the N between the influent and effluent; the N remained in the wastewater. A recent study on a Texas dairy using a two-chamber gravity separation system shows a minor reduction of 10% less N in wastewater effluent (Mukhtar et al., 2011). Mechanical separators, by comparison, separate a greater fraction of the N into solids. Data suggest that mechanical separators separate as much as 51% of total Kejdal N into solids, but particle size governs the actual efficacy (Zhang and Westerman, 1997). As one might expect, mechanical separators are less capable of transferring N contained in smaller particles. Addition of various chemicals to wastewater enhances solid and liquid separation. Synthetic polymers (flocculants) coagulate fine particulates, which then settle over time. Common flocculants are often related to polyacrylamide (PAM) which has also been used in irrigated cropland to reduce runoff of sediments and nutrients (Barvenik, 1994). Experiments have demonstrated their effectiveness for aggregating N into the solid manure (Hannah and Stern, 1985). Zhang et al. (1998) show that adding ferric chloride and a polymer to dairy manure in California can remove 67 to 69% of N from liquid. Sedimentation basins and mechanical separation systems are common practice on California dairies (Meyer et al., 1997). More than 63% of dairies used some form of manure separation technology in 2007 (Meyer et al., 2011). Manure separation with sedimentation basins, mechanical separators, flocculants, or a combination of the practices provides greater



control over manure N. At production scale, separation creates additional requirements for labor and equipment. Refining and cleaning the equipment and the basins requires intensive management, with the management intensity being correlated with technology sophistication. However, current levels of adoption suggest utilization is practically feasible for operators. More detailed information will be needed to optimize their utilization and understand their benefits for N cycling. 7.1.9.4 Compost Manure Solids and Other Organic Materials

Composting—the anaerobic digestion of wastes—stabilizes N contained within organic wastes by transferring it into soil organic matter, where it is less available to soil microorganism and hence vulnerable to loss. Although often ignored, even under ideal composting conditions a fraction of the N in the compost is released as NH3 and N2O during biological immobilization and through chemical reactions and thus composting can contribute to atmospheric and climate concerns (Ahn et al., 2011). The fate of N during waste composting is subject to the physical and chemical composition of the compost pile: aeration, C/N ratio, moisture, pile structure, pH, and temperature. Through modification of these variables, facility operators can control the rate of digestion. Differential management changes the physical properties of the pile and by extension, N emissions. Evidence suggests that N2O emissions are nearly double in turned windrows than in static piles, 2% versus 1% of N (Ahn et al., 2011). Increased emissions are possibly the result of redistribution of N throughout the pile and greater gas diffusion. The multitude of driving factors and the controlled environment suggest there are likely opportunities to conserve N in composts by changing management. Composting represents an important component of California’s N cycle. It is one of the fundamental steps prior to recycling nutrients in organic wastes to land. Manures and urban green wastes are already widely composted throughout California, with the vast majority (77%) of composting facilities using turned windrows (TFASP, 2005). Despite the uniformity of method, individual composters manage the piles to different degrees. That suggests improved compost pile management may provide an opportunity to mitigate N emissions.



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Zhang, R.H., Westerman, P.W., 1997. Solid-liquid separation of animal manure for odor control and nutrient management. Applied Engineering in Agriculture 13.

Zhang, X., Liu, X., Zhang, M., Dahlgren, R.A., Eitzel, M., 2010. A Review of Vegetated Buffers and a Meta-analysis of Their Mitigation Efficacy in Reducing Nonpoint Source Pollution. Journal of Environment Quality 39, 76. doi:10.2134/jeq2008.0496

Zhu-Barker, X., Horwath, W.R., Burger, M., 2015. Knife-injected anhydrous ammonia increases yield-scaled N2O emissions compared to broadcast or band-applied ammonium sulfate in wheat. Agriculture, Ecosystems & Environment 212, 148–157. doi:10.1016/j.agee.2015.06.025

CHAPTER SEVEN


Appendix 7.2 Supporting Material: Explanation of Calculations and Evaluating Uncertainty

Lead Authors:

T.S. ROSENSTOCK





Suggested citation:

TS Rosenstock, S Brodt, M Burger, H Leverenz, and D Meyer. “Appendix 7.2: Supporting Material: Explanation of Calculations and Evaluating Uncertainty.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 7.2 SUPPORTING MATERIAL 2

7.2.0 Introduction

The calculations underlying our estimates of changes to California’s nitrogen (N) cascade from adopting the strategic actions in Chapter 7 are explained here. Estimates are generally calculated as the difference between the baseline N flows established by A California nitrogen mass balance for 2005 (Chapter 4), and technically feasible relative changes set by research. Whenever possible, estimates of the expected changes with improved practices rely on data and emissions factors derived from California-specific peer-reviewed, grey literature, and novel data compilations completed as part of this assessment. A few calculations, however, require emissions data for which California-specific studies were unavailable. Under those instances, we applied the most widely accepted values. A list of the emissions factors used and their sources can be found in Table 7.2.1. Table 7.2.1. Emissions Factors and Agricultural Sources Used in Calculations. Emissions factors and sources Emission CA-specific Source Global Source NH3 from manure1 35% [20, 50] Chang et al. (2005) N2O from manure 2.0% IPCC (2007) NH3 from fertilizer 3.2% ± 2.4 Krauter and Blake (2009) N2O from fertilizer 1.4% This assessment 1.0% IPCC (2007) N2O from leguminous crops 1.0% IPCC (2007) NO3

- leaching from alfalfa 8.2% Letey et al. (1979) Putnam et al. (2005)

NO3- leaching from

croplands 34% This assessment 30% IPCC (2007)

1Includes emissions from animal production unit and field operations.



7.2.1 Agricultural Nitrogen Use Efficiency

7.2.1.1 Crop Production1

Measures of nitrogen use efficiency (NUE) are ratios of the amount of nitrogen (N) assimilated to the amount applied. Assuming output remains constant, increased NUE will result in less N fertilizer applied. We compiled data from published and unpublished research results to estimate N use efficiency by partial nutrient balance (PNB) for the 22 most economically important California crops2 (Table 7.2.2). After eliminating the zero-N and excessive N treatments common in N rate trials, we used the median values for yield and N application rate as reasonable benchmarks for the potential PNB with improved practices. We ignored the low and high N rate treatments because of their potential to bias the median value. Data on fertilizer application rates by crop was taken from Rosenstock et al. (2013) to create a weighted average of N use for each crop group and then the USDA acreage of aggregated crop groups was used to estimate the total potential change. Avoided emissions due to the reduced N fertilizer use are discussed in relevant sections.

1 Raising NUE on croplands affects indirect emissions from fertilizer production and transport. But it also has concordant impacts on N2O emissions and NO3 leaching and thus this section also describes methods used to calculate reduction potentials for “Nitrate leaching from croplands” (Section 7.2.2) and "Greenhouse gas emissions from fertilizer use” (Section 7.2.3). 2 PNB is used in lieu of other measures of NUE because of the need to compare statewide average data to research results that may or may not have been specifically designed to test NUE. PNB does not discriminate between the source of N, be it from soil mineralization or fertilizer. Thus, the values derived from literature may be an overestimate if soils were fertile or underestimate if they were not. Regardless, a basic premise of sustainable nutrient management is to balance nutrient exports with applications.



Table 7.2.2. Current and Improved Partial Nutrient Balance (PNB) for Major California Crops. PNB is the ratio of N in crop material exported from field to the amount of N fertilizer applied. Crop Current

PNB (%)# Improved PNB (%)# Sources for improved PNB

Low High Median Cotton 61 40 93 66.5 Fritschi et al. (2005) Potato 55 27 91 59 Meyer and Marcum (1998) Rice 82 40 129 88 Linquist et al. (2009) Wheat 56 68 104 86 Ehdaie and Waines (2001) Almond 49 69 118 82.5 Muhammad et al. (2015) Avocado 19 31 45 38 Lovatt et al. (2001) Grapes, raisin 45 54 70 62 Peacock et al. (1991)

Grapes, wine 56 36 93 64.5 Smart (pers. comm.), Christensen et al. (1994)

Lemons 51 52 - 52 Embleton et al. (1981) Nectarines 22 32 103 67.5 Weinbaum et al. (1992)

Oranges 39 44 - 44 Embleton et al. (1974), Ali and Lovatt (1994)

Peaches, freestone 25 17 63 40 Saenz et al. (1997), Johnson et al. (2001)

Pistachio 56 72 - 72 Rosenstock et al. (2010) Plums, dried 54 14 54 34 Southwick et al. (1996) Walnut 52 41 151 96 Richardson and Meyer (1990) Broccoli 46 35 42 38.5 LeStrange et al. (1996) Carrots 27 62 75 68.5 Allaire-Leung et al. (2001) Celery 36 41 71 56 Hartz et al. (2000) Lettuce 34 51 - 51 Hartz et al. (2000) Peppers, bell 18 24 36 30 Hartz et al. (1993) Strawberry 34 54 55 54.5 Bendixen et al. (1998) Tomatoes, fresh market 61 61 84 72.5 Hartz et al. (1994)

Tomatoes, processing 64 89 108 98.5 Hartz and Bottoms (2009)

Corn1 61 - - - No data available for improved PNB



7.2.1.2 Animal Production

The capacity to improve feed N efficiency in California animal production was based on two studies of feeding practices in dairies. Surveys of six and fifty-one dairy operators have separately been conducted, one in each of the two major dairy producing areas of the state (Tulare and Modesto), respectively. Their results are consistent with each other, average milk N utilization efficiency equals 23% and efficiency ranges between 27% to 30% for more efficient producers (Meyer and Robinson, 2007; Castillo et al., 2005). The difference between average and more efficient producers suggests that a conservative estimate of the potential to raise milk N utilization efficiency in dairy production is four percentage points.

We assumed that changing N utilization efficiency would not affect milk yield and that milk N concentration is reasonably constant. Therefore we can calculate the change in efficiency on feed N demand by the following TMN = FN * FE, where, TMN equals total milk N, FN equals the feed N, and FE equals the feed N efficiency. By this equation and the assumptions about milk N concentration and milk N yield, a four percentage point increase in N utilization efficiency would reduce feed N demand to 85% of current levels. We then calculated the potential changes in the N cascade from reduced feed demand. We assumed dairy cows eat a diet consisting of 50% legume and 50% grain. Only non-leguminous feed crops were assumed to be produced with fertilizer. Crops produced with fertilizer were produced with an NUE of 45% to calculate fertilizer applied3. Leaching and gaseous emissions were calculated based on N applied for crops receiving fertilizer and a ratio of 30 kg N per ha leached for 360 kg fixed N4. We assumed the data and methods developed for dairy cows are applicable for all animal production systems and hence total feed N demand in California. Extending the results from dairy production systems to all animals is reasonable for two reasons. One, feed requirements of dairy cows dominate total feed N in the state, accounting for 81% of total demand. Two, and

3 NUE based on California specific data described in Chapter 3. Average NUE values in California are generally similar to those found in other regions (Cassman et al., 2002). 4 30 kg N leached is based on average leaching values in alfalfa measured in tile drains of California by Letey et al. (1979). 360 kg per ha is average production on alfalfa (Putnam et al., 2005).



perhaps more importantly, the target change in milk N utilization efficiency was low, four percentage points, and likely represents a lower bound for other animal production systems in the state. For example, Nahm (2005) suggests that a greater than 60% N utilization efficiency is achievable in poultry, yet California mass balance calculations and research of similar systems from other locations suggest N utilization efficiency is less than 40% (Chapter 4; Neijat et al., 2011).

7.2.2 Ammonia Volatilization from Manure

With increased feed N efficiency, N excretion will decrease. We applied a simple linear equation developed in a California feed study to estimate the changes in nitrogen (N) excretion with changes in efficiency. The impact of decreased feed intake on excretion was calculated following Castillo et al. (2005): N excretion = 0.9*N intake – 89 A certain degree of volatilization from manure is inevitable, but manure management practices have a large impact on the quantity released (Rotz, 2004). The Committee of Experts of Dairy Manure Management5 (Chang et al., 2005) estimates emissions in the Central Valley by three different methods. By their measures, total volatile emissions from the production area and land application combined are likely to be between 25% and 50% of total excreted N6. The emission rate from any single operation may occur throughout this range and it is reasonable to assume that the extremely high and low emission rates occur less frequently. Therefore, the distribution of volatilization rates across the near 2000 dairies in California can be approximated by a normal distribution with mean 37.5 and standard deviation 6.75. That means that emissions rates for 95% of the operators will fall within 25% to 50%, with most operations emitting around 37.5% of excreted N as NH3. It also means that producers operating in the top quartile of

5 Volatilization is correlated with N excretion (James et al., 1999; Oenema and Tamminga, 2005). Estimates are again based on dairy production because it is responsible for the vast majority of manure N and by extension manure derived atmospheric NH3. Emissions per unit of product for cattle on feed and poultry are likely to be higher and lower, respectively because of efficiencies and excretion. 6 The findings suggest that a California dairy emits 20% to 40% of excreted N as NH3 from the production unit itself. Field emissions of 10% to 20% of NH4-N or 5% to 10% excreted manure N, assuming 50% of the N in manure is in this form.



production emit approximately ≤ 33%. Since 25% of the operators are already operating above this level, we presume that it is technologically feasible to shift the total distribution in two ways. One, improve management that shifts the mean emission rates from 37.5% to 33%. Two, narrow the range of emissions by reducing the standard deviation to 3.5. The latter shift means that 95% of producers will volatilize between 26% and 40% of excreted N (Figure 7.2.1). Narrowing the distribution in this way assumes that there is a theoretical limit for potential best practice (approximately 25%) and excessive emitters have the greatest potential to improve (roughly 20% decrease) (Figure 7.2.1).

FIGURE 7.2.1 Distributions Used to Calculate Potential Reduction of NH3 Volatilization from Manure Handling. Current practice based on dairy production in the Central Valley.

We then created a simulation program, coded in the statistical program R, that estimated manure N volatilization. First, we randomly sampled each distribution one time for each dairy in the state to obtain the projected emissions rates for each dairy. Second, we multiplied each rate by the amount of manure N produced at an average dairy7. Third, we subtracted the N volatilization from the improved from the original practice. We repeated this program 5000 times to obtain a mean and range of potential values of NH3 emissions reductions.

7 For simplicity, we assumed that dairies all had equal number of cows.



7.2.3 Nitrate Leaching from Croplands

Nitrate leaching reductions equal the sum total of avoided NO3 leaching losses from improved management of inorganic and organic nitrogen (N) sources on croplands. NO3-N = [NF * ΔPNBINORG * EFCA NO3 INORG] + [NMAN * ΔPNBorg * EFCA NO3 ORG] where, NO3-N = annual amount of avoided NO3-N losses from croplands NF = estimated inorganic fertilizer application on croplands ΔPNBINORG = change in the ratio of N in crop material exported from the field to the amount of N applied from inorganic sources, expressed as a decimal EFCA NO3 INORG = NO3 leaching emissions factor for California with inorganic fertilizer NMAN = amount of organic manure applied to croplands ΔPNBorg = change in the ratio of N in crop material exported from the field to the amount of N applied from organic sources, expressed as a decimal EFCA NO3 ORG = emissions factor derived from research with organic sources in California, median of solid and liquid manure assume a 50-50 split in handling NO3 = NO3-N * (62/14) Inorganic fertilizers: Avoided leaching losses were estimated as the amount not leached due to increased NUE (see above) multiplied by the California-specific emissions factor developed as part of the California Nitrogen Assessment, 34% of N applied, a slightly higher amount than suggested by the IPCC, 30% of N applied. NO3 INORG = ΔNUE * fertilizer N * EFCA LEACHING * Molecular conversion Organic fertilizers: we assume organic fertilizers (animal manures, composts) are currently being applied at an average of 60.5% PNB or 1.65x plant uptake. It is important to note that 1.65x uptake may represent unrealistic goals for agricultural systems using organic N fertilizers; however, this value represents the maximum bound set by the Central Valley Regional Water Quality Control Board (CRWQCB, 2011) and thus presents a reasonable baseline for this



theoretical discussion/analysis. Emission reductions result from decreasing applications to 1.4x plant uptake or 71% PNB, as follows in the equation: NO3 INORG = Manure N * ΔPNB * EFCA LEACHING

7.2.4 Greenhouse Gas Emissions from Fertilizer Use

Nitrous oxide reductions equal the sum total of avoided N2O losses from improved inorganic fertilizer management on croplands. N2O = [NF * ΔPNBINORG * EFCA N2O] * Molecular conversion where, N2O = annual amount of avoided N2O-N losses from croplands NF = estimated inorganic fertilizer application on croplands ΔPNBINORG = change in relative amount of N uptake from inorganic sources, expressed as a decimal EFCA N2O = N2O leaching emissions factor for California with inorganic fertilizer 7.2.5 Nitrogen Oxide Emissions from Fuel Combustion

As part of the Statewide Implementation Plan (SIP) to achieve ozone and PM2.5 attainment, CARB developed estimates of potential NOx reductions. We report their estimates for 2014 for the San Joaquin Valley and South Coast and 2018 for the Sacramento Valley as measures of potential emissions reductions within the current technology and policy envelope (Table 7.2.3). Table 7.2.3. Estimated Reductions in Nox And PM2.5 from the Implementation of Proposed 2007 Measures by CARB (Tonnes Year-1). Estimates for the South Coast and San Joaquin Valley are for 2014 and for the Sacramento Valley are for 2018. NOx Direct PM2.5

Source activity

South Coast

San Joaquin Valley

Sacramento Valley

Total

South Coast

San Joaquin Valley

Total

Passenger vehicles1 662 0 0 662 - 0 0



Heavy-Duty Trucks2 19,766 21,720 3,145 44,631 - 1,424 1,424 Goods Movement Sources3

9,635 0 99 9,734 861 - 861

Off-Road Equipment4 3,476 1,225 629 5,330 861 265 1,126 Total Projected Emission Reductions

33,540 22,945 3,874 60,359 2,880 1,689 4,569

1Smog check improvements. 2Cleaner in-use heavy duty truck. 3Ship auxiliary engine cold ironing and clean technology; cleaner main ship engines and fuel; clean up existing harbor craft. 4Cleaner in-use off-road equipment (>25 hp).

7.2.6 Wastewater Management

7.2.6.1 Wastewater Treatment Plants

Approximately 90% of wastewater is processed at centralized wastewater treatment plants. Currently, our best estimate is that 50% undergoes nitrogen (N) treatment (Chapter 3)8. Therefore, we assume that 50% of the total wastewater N load passing through wastewater treatment plants (161.1 Gg N) is treated and is denitrified at a rate of 97%9 already (78.1 Gg N). A reasonable near-term goal may be a 10% increase in treatment to 60% of influent. From that assumption, an additional 16.1 Gg would be treated, equating to 15.6 removed from wastewater and denitrified to N2 and 0.5 Gg N released as N2O. We ignore the indirect emissions from denitrification that occurs in N rich ocean environments (Seitzinger et al., 2006).

7.2.6.2 Onsite Wastewater Treatment Systems

Few onsite wastewater treatment systems (OWTS) in use today directly treat for N. Without treatment; it is reasonable to expect minimal, say 4% N attenuation. By comparison, current OWTS designed to remove N achieve 40% N removal rates, at least—but often much higher (Division of Environmental Health, 2009). We calculated the effects of switching to improved OWTS via the following: 8 It is not possible to know, given the available data, to what extent facilities equipped with N removal capacity utilize it. 9 97% efficacy is used to account for fraction of N2O produced. One review suggests emissions rates between 1 and 5%; we used the median of 3%.



NOWTS = NC-OWTS * (1 - Rb.OWTS)

and ΔNOWTS = (NC-OWTS * (1-Ri.OWTS)) - (NC-OWTS * (1 - Rb.OWTS)) where, NOWTS = N loading from OWTS NC-OWTS = Current N loading (17.9 Gg N, 10% of wastewater N) Rb.OWTS = Removal rate of current systems10 Ri.OWTS = Removal rate with improved technology Only a fraction of the systems in use—poorly sited or mismanaged—need to be retrofitted or replaced because of their prospect to degrade natural resources. Currently, the total number of systems needing reconditioning is unknown. We therefore calculated changes in emissions for a range of reconditioning (20%, 40%, 80%) and removal efficacy (40%, 60%, 80%). This provides a quantitative range of the potential emissions reduction that might be expected (Table 7.2.4). Table 7.2.4. Estimated Reductions in N from Improved OWTS Management (Gg N). Estimates were done across a range of removal efficiencies and retrofit scenarios to account for variation in system management and the fact that not all OWTS present an environmental risk and need to be replaced. By comparison, raising treatment at WWTP 10% reduces Nr by 15.9 Gg yr-1. Retrofit (% of existing systems) Removal efficiency (%) 20 40 80 40 1.3 2.6 5.2 60 2.0 4.0 8.0 80 2.7 5.4 10.9

10 Note Rb.OWTS is assumed to be zero in Chapter 4. Here we assume a limited amount of natural environmental attenuation.



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CHAPTER SEVEN


Appendix 7.4 Lifecycle Accounting and Pollution Trading: Next Generation Decision-Making

Lead Authors:

T.S. ROSENSTOCK





Suggested citation:

TS Rosenstock, S Brodt, M Burger, H Leverenz, and D Meyer. “Appendix 7.4: Lifecycle Accounting and Pollution Trading: Next Generation Decision-Making.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 7.4 LIFECYCLE ACCOUNTING AND POLLUTION TRADING 2

7.4 Lifecycle Accounting and Pollution Trading: Next Generation Decision-Making

Control technologies have historically been and, for the most part, are still evaluated based on their ability to impact or regulate specific nitrogen (N) species from a particular source. Emphasis on individual transfers of N, without systemic consideration of the entire N cascade, can result in exchanging one N pollutant for another (as discussed in Chapter 7, Section 7.2 of the California Nitrogen Assessment). Risks of pollution swapping extend throughout the supply chain and can even induce non-N pollutants. The wider environmental context needs to be considered to determine the value and appropriateness of a control technology. Unintended consequences may result when practice efficacy is defined too narrowly.

To begin with, the N cascade is inextricably linked with the carbon (C) cycle. As a result, fertilizer and food production, transportation and industrial combustion, soil processes, and waste processing and disposal affect both biogeochemical cycles simultaneously. The implication is that, in many cases, the perturbation of one cycle cannot be fully assessed without including effects on the other and implementation of risk reduction strategies can create tradeoffs among emissions of various elements.

A lot has been made of the interaction between C and N in terms of climate change and agriculture, with the value of practices that at first were thought critical to agriculture’s response being heavily scrutinized; no-till or minimum tillage is one notable example. Cooling benefits of accumulation of soil C by minimum tillage has been called into question, with some evidence suggesting benefits are offset by increases in the much more potent N2O; however, the effects are far from certain (Baker et al., 2007; Butterbach-Bahl et al., 2004; Six et al., 2004). Tillage presents an example of tradeoffs in direct field emissions, but tradeoffs among indirect emissions of greenhouse gases may also occur. Draining rice fields mid-season to control methane emissions has been cited as a possible mitigation option (Eagle et al., 2010). When soils dry out, oxygen diffuses into the soil allowing the soils to go from anaerobic to aerobic, reducing methane. But the transition of soil water content presumably would create conditions conducive to denitrification. Regardless if direct field emissions of N2O increase, the added machine time necessary to manage the field—draining and reflooding, increased herbicide applications, etc.—would increase CO2 emissions from fuel combustion. Consideration of the entire suite of emissions associated with changes in production is needed to support notions of mitigative technologies.



The agricultural examples illustrate the need to account for emissions of N and C across the entire life cycle of a production system to differentiate among practices. Much has been made of the value of such assessments, with diverse institutions from private companies (e.g., Tropicana Orange Juice) to international organizations such as the FAO (e.g., Livestock’s Long Shadow and its follow-up) utilizing them. However, often the comparisons are rife with controversy. Disagreement stems from where the system boundaries are drawn and the underlying assumptions of the life cycle model. One of the most high profile examples is from the highly controversial report titled, “Livestock’s Long Shadow” (Steinfeld et al., 2006). The report states that the radiative forcing of the global livestock industry is greater than the impact from transportation. The report, however, compared emissions from feed to fork for livestock but only the direct emissions from fuel combustion for transportation, and not all the indirect emissions associated with fuel extraction, processing, and distribution. Thus, concerns have been raised about the appropriateness of the appraisal (Mitloehner et al., 2009). In recent years, progress has been made toward standardizing methodologies, such as with the International Organization for Standardization’s life cycle assessment and carbon footprint standards (www.iso.org), as well as Product Category Rules for creating Environmental Product Declarations (environdec.com), voluntary disclosures of environmental impacts of specific commercial products that are increasingly being used in the food industry. Nevertheless, for actual quantification of various N-related flows, such as N2O emissions, that become part of such life cycle assessments, Kendall (personal communication) has found little consistency in the methods used. Therefore, we conclude that there is clear value and need to evaluate practices based on life cycle assessment. At the same time, evaluation and further refinement of the methods used to quantify impacts will add to their value.

Because of the need of full accounting of greenhouse gas emissions, it is important to note that direct field emissions account for only a fraction of total climate forcing from fertilizer use. So-called indirect emissions, those that don’t occur from within the field of application boundaries, can be quite significant. Prior to the field application, production and transport of fertilizer generates a small amount of N2O, but large amounts of carbon dioxide because of the energy demand for N fixation via the Haber Bosch process (See Box 5.4.2). After application, there are many pathways for N loss. When it moves beyond the field, it is still likely to produce N2O emissions. In some cases, such as riparian environments, probability of emissions increases as conditions become more conducive (saturated soils). Crutzen et al. (2008) suggest that when up and downstream effects of agriculture are included in the accounting, 3 - 5% of applied fertilizer is given off as N2O, more than double the amount of direct emissions.



References Baker, J.M., Ochsner, T.E., Venterea, R.T., Griffis, T.J., 2007. Tillage and soil carbon

sequestration—What do we really know? Agriculture, Ecosystems & Environment 118, 1–5. doi:10.1016/j.agee.2006.05.014

Butterbach-Bahl, K., Kesik, M., Miehle, P., Papen, H., Li, C., 2004. Quantifying the regional source strength of N-trace gases across agricultural and forest ecosystems with process based models. Plant and Soil 260, 311–329.

Crutzen, P.J., Mosier, A.R., Smith, K.A., Winiwarter, W., 2008. N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels. Atmospheric Chemistry and Physics 8, 389–395.

Eagle, J.E., Henry, L.R., Olander, L.P., Haugen-Kozyra, K., Millar, N., Robertson, G.P., 2010. Greenhouse gas mitigation potential of agricultural land management in the United States. A Synthesis of the Literature. Technical Working Group on Agricultural Greenhouse Gases (T-AGG) Report.

Mitloehner, F., Sun, H., Karlik, J., 2009. Direct measurements improve estimates of dairy greenhouse-gas emissions. California agriculture 63, 79–83.

Six, J., Ogle, S.M., Breidt, F.J., Conant, R.T., Mosier, A.R., Paustian, K., 2004. The potential to mitigate global warming with no-tillage management is only realized when practised in the long term. Global Change Biology 10, 155–160. doi:10.1111/j.1529-8817.2003.00730.x

Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., De Haan, C., 2006. Livestock’s long shadow. Food and Agriculture Organization of the United Nations, Rome.

CHAPTER SEVEN


Appendix 7.5 Metrics for Nitrogen Management

Lead Authors:

T.S. ROSENSTOCK





Suggested citation:

TS Rosenstock, S Brodt, M Burger, H Leverenz, and D Meyer. “Appendix 7.5: Metrics for Nitrogen Management.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 7.5 METRICS FOR NITROGEN MANAGEMENT 2

7.5 Metrics for Nitrogen Management

Our understanding of the current state and changes in the nitrogen (N) cascade relies on measurement of N in the environment. N measurements are typically expressed in terms of mass loading (e.g., kg NO3 per ha) or concentration of a particular form of N (e.g., ppm NO3). Data collected quantifying these metrics of N can then be translated into management strategies, policy recommendations, and regulations. Smart N metrics capable of documenting the conditions of California’s N cascade (at an appropriate scale and reasonable cost) are therefore central to the development of response strategies. What forms of N are measured and where they are measured can influence the interpretation of the impacts and the response options. For example, field-scale mass balance suggests groundwater recharge from only a few cropping systems in California leach a mass of N that would meet the maximum contaminate load standards of a concentration of 10 mg/L NO3-N (approximately 35 kg N per ha at average recharge rates) that has been set to ensure safe drinking water (Harter et al., 2012). However, N in groundwater recharge may be attenuated through denitrification or diluted through increased irrigation or precipitation. Changes in N concentration during its transmission to groundwater suggest that where in the soil profile N is measured is important in understanding its actual impacts on drinking water. Defining metrics and designing measurement and monitoring programs should be tied to impacts of N on the environment and the delivery of ecosystem services. The nature and magnitude of impacts are dependent upon the sources of N, the media (air, soil, or water), and the chemical forms of N. It is important to note that the relationships between sources and impacts are not straightforward. Only in some cases does the source of N largely determine its transmission in certain forms into certain media. In many cases, however, a single source contributes to multiple N concerns simultaneously–directly and indirectly. A balance must be struck between placing emphasis on measuring primary sources versus measuring subsequent cascading effects. Historically, measurements have informed management and policy to help maintain N impacts below an acceptable threshold of risk. When a contaminant is found to have a direct correlation with environmental or health outcomes, control mechanisms can be put in place to limit the damage. Statewide ozone standards are one example of this approach. The California Air Resources Board (CARB) and air basins monitor air quality for ozone concentrations and suggest citizens take precautionary measures when concentrations exceed safe levels. A similar approach is used–though less frequently–as part of the water monitoring programs. Though



effective, the concern is that addressing single impacts in isolation ignores the intertwined dynamics of the N cascade. For some cases and in some locations, a multi-impact management approach may be appropriate (e.g., Tulare Lake Basin with its poor groundwater quality, high ozone levels, and high N deposition). Not all metrics address only a single N source or impact (e.g., NOx concentrations). Collective metrics that aggregate across end points are available for some environmental impacts, with additional ones just coming into use. Perhaps the most well-known collective metric is applied global warming and greenhouse gas emissions. Methane, nitrous oxide, and carbon dioxide emissions can all be expressed in terms of their radiative forcing over a fixed time-frame (100 years) in a common unit, ‘carbon dioxide equivalents.’ Unifying the metric allows management practices that affect various impact pathways to be compared. Collective metrics are also used to define acidification (e.g., SOx and NOx as H+ equivalents). Clearly it is possible and potentially advisable to present collective metrics when multiple factors affect a single impact. Often, however, a single source affects multiple impacts in opposite directions, so that tradeoffs exist, for example, between food production and climate change. Here as well, collective metrics may be able to capture the relationships between the impacts. Recently, the global warming intensity of cropping systems (yield-scaled global warming potential) has gained traction in agronomic discussions because it scales the emissions by crop yield, acknowledging that some emissions are necessary in highly productive agricultural systems and food production is critical to survival. While the research community has begun to adopt this collective metric, it is yet to be integrated into policy or management approaches. The relatively slow adoption rate illustrates the speed at which a collective metric might come into use outside of research. Despite the slow transition, global warming intensity presents a good example of the type of innovation that will be needed to address multiple N impacts in a systematic way. Metrics are fundamental to any N response strategy. California has the infrastructure needed to form the basis of a useful N monitoring program (see Appendix 7.6). However, coupling innovative metrics to the realities of the N cascade is still a challenge. Further, integrating information that can quickly and in near real-time feed back into the management and policy process is the next frontier in addressing N issues in California. Reference Harter, T., Lund, J.R., Darby, J., Fogg, G.E., Howitt, R.E., Jessoe, K., Pettygrove, G.S., Quinn, J.,

Viers, J.H., Boyle, D.B., Canada, H.E., De La Mora, N., Dzurella, K.N., Fryjoff-Hung, A., Hollander, A.D., Honeycutt, K., Jenkins, M.W., Jensen, V.B., King, A.M., Kourakos, G.,



Liptzin, D., Lopez, E., Mayzelle, M.M., McNally, A., Medellín-Azuara, J., Rosenstock, T.S., 2012. Addressing Nitrate in California’s Drinking Water: With a Focus on Tulare Lake Basin and Salinas Valley Groundwater: Report for the State Water Resources Control Board Report to the Legislature. Center for Watershed Sciences, University of California, Davis.

CHAPTER SEVEN


Appendix 7.6 Toward a Unified Monitoring Strategy for California’s N Cascade

Lead Authors:

T.S. ROSENSTOCK





Suggested citation:

TS Rosenstock, S Brodt, M Burger, H Leverenz, and D Meyer. “Appendix 7.6: Toward a Unified Monitoring Strategy for California’s N Cascade.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 7.6 TOWARD A UNIFIED MONITORING STRATEGY FOR CALIFORNIA’S N CASCADE 2

7.6 Toward a Unified Monitoring Strategy for California’s N Cascade

A comprehensive monitoring network and information system is needed to understand and shape California’s nitrogen (N) cascade. The primary function would be to provide information in practical and useable formats on the status of N stocks and flows, ecological and human health impacts, and feedback information to assess the efficacy of policy interventions.

Fortunately, California has the makings of a robust monitoring network already in place. Regulatory agencies operate monitoring stations with the capacity to detect major N compounds and their derivatives. The most developed monitoring network is for air quality, with more than 100 monitoring sites operated by CARB and the 13 regional air basins cataloging ambient ozone, PM2.5, and nitrogen dioxide concentrations. Deposition of N compounds (NH3, NOx), however, is less well observed. Less than twenty active monitoring stations, sparsely distributed throughout the state, catalog dry and wet deposition of N species through the EPA Clean Air Status and Trends Network and the National Atmospheric Deposition Program. In addition, water quality programs, including ones headed by the US Geological Survey, State Water Resources Control Board, Regional Water Quality Control Boards, the CA Department of Public Health, and concerned citizen groups, monitor NO3

- concentrations at wellheads, in freshwater streams and lakes, groundwater, and coastal regions. Monitoring activities of the numerous agencies identified provide a sound basis for assessing conditions and change in N species.

Tracking sources of N is more difficult. This is largely because the majority of N emissions are non-point source by nature. Observing both the extent and intensity level of non-point source activities is almost impossible on a large scale. Fertilizer use is a prime example. Whilst CDFA collects data on fertilizer sales, it provides little reputable information about when, where, and how much N is used, all factors that decidedly determine the impacts on the environment. Even when the necessary information is collected, it may not be made available publically. The Dairy General Order requires producers to report the N applied by field, but the information resides on hard copies within the board’s office and is not public record at this time. By contrast to non-point sources, data are widely available on point sources, including emitters like industry (e.g., food processors) and wastewater treatment plants. Even with point sources, however, access is still limited by the fact that data often reside in disparate locations and difficult-to-access forms.

Development of a unified, transparent knowledge management system to integrate information from the monitoring networks would be an important step to developing technical and policy response strategies. State and national programs collect information without


APPENDIX 7.6 TOWARD A UNIFIED MONITORING STRATEGY FOR CALIFORNIA’S N CASCADE 3

synthesizing it, despite the multi-source and multi-impact nature of the N cascade. Development of mechanisms that allow exchange and synthesis of data will facilitate the development of targeted multi-media response strategies. With data more easily accessible as well as assessable to decision-makers, new insights on priorities may be generated. Researchers would benefit as well. A comprehensive data management system would provide easy access to historical and current public records. When coupled with an assessment of environmental and human health impacts, a comprehensive data system facilitates identification of clear research gaps and areas of concern. Development of a unified strategy that integrates monitoring and data management would foster novel insights and support informed decisions for managing the N cascade.

CHAPTER EIGHT

Responses: Policies and Institutions

Appendix 8.1 Experience with Nitrogen Policy Instruments in Practice: Case Studies

Lead Authors:

K. BAERENKLAU AND T.P. TOMICH


S. DAROUB, A. DREVNO, V.R. HADEN, C. KLING, T. LANG, C.-Y. LIN, C. MITTERHOFER, D. PARKER, D. PRESS, T. ROSENSTOCK, K. SCHWABE, Z. TZANKOVA, AND J. WANG



Suggested citation:

K Baerenklau, TP Tomich, S Daroub, A Drevno, VR Haden, C Kling, T Lang, C-Y Lin, C Mitterhofer, D Parker, D Press, T Rosenstock, K Schwabe, Z Tzankova, and J Wang . “Appendix 8.1: Experience with nitrogen policy instruments in practice: Case studies.” Online appendices for California Nitrogen Assessment: Challenges and Solutions for People, Agriculture, and the Environment. TP Tomich, SB Brodt, RA Dahlgren, and KM Scow, eds. Agricultural Sustainability Institute at UC Davis. (2016). asi.ucdavis.edu/nitrogen.


APPENDIX 8.1 EXPERIENCE WITH NITROGEN POLICY INSTRUMENTS IN PRACTICE: CASE STUDIES 2

8.1 Introduction: Experience with Nitrogen Policy Instruments in Practice

We consider a total of twelve case studies: five California programs, five nutrient-impaired waterbodies in other states, an overview of European nitrogen (N) policies, and a previously published review of state-level nutrient programs. The last of these is qualitatively different from the others and includes both program assessments as well as recommendations for the future. The case studies offer insights into and lessons learned from the more commonly used policy approaches as well as some information about other less commonly used policy instruments. Contents: 8.2.1 California’s Nonpoint Source Program 8.2.2 California’s Agricultural Water Quality Grants Program 8.2.3 California’s Central Coast Agricultural Waiver Program 8.2.4 California’s Dairy Nitrogen Regulations 8.2.5 California’s Regulation of Atmospheric Nitrogen Emissions 8.2.6 North Carolina’s Neuse River Basin 8.2.7 The Mississippi-Atchafalaya River Basin 8.2.8 Maryland’s Nutrient Management Program 8.2.9 Florida’s Everglades 8.2.10 Pennsylvania’s Conestoga River Watershed 8.2.11 The European Experience 8.2.12 USEPA Review of Selected Nutrient Programs 8.2 Case studies

8.2.1 California’s Nonpoint Source Program

California’s Nonpoint Source (NPS) Program regulates many types of pollutants that originate from diffuse sources and that potentially impact surface and ground waters of the state. As has been documented extensively in this assessment, it is well established that agriculture is a major source (greater than 50%) of nonpoint source nitrogen (N) discharges to groundwater and a moderate source (between 25% and 50%) of N discharges to surface water, and thus it also follows that agriculture is a significant contributor to the associated N-related impacts on those resources (See Chapter 4).

The primary law that establishes authority for regulating agricultural nonpoint sources of N pollution in California is the Porter-Cologne Act. Under the Act, the SWRCB and the



RWQCBs are authorized to establish water quality control plans (called “basin plans” at the regional level) and to issue discharge permits (called Waste Discharge Requirements, or WDRs) and conditional waivers of those permits. Each source must comply with any discharge prohibitions specified in the relevant basin plan and/or the terms of a WDR or a conditional waiver. If a source is found to be in violation of any of these requirements, the state and regional boards are authorized to take enforcement actions including notices to comply, civil penalties and referrals for criminal penalties (SWRCB and CA EPA, 2004).

These three administrative tools—discharge prohibitions, WDRs and waivers of WDRs—provide the basis for regulating agricultural nonpoint sources of N pollution. While discharge prohibitions and WDRs may specify the conditions under which N discharges are allowed (if at all), they may not specify the means by which sources will achieve compliance. Thus these tools appear to be emission-based. However, discharge prohibitions and WDRs may be written such that the only practical means of compliance is to implement a prescribed set of best management practices (BMPs, or MPs in the California regulations). Furthermore, conditional waivers of WDRs may require that a particular set of MPs must be implemented. And moreover, assessment of the program focuses primarily on monitoring MP implementation and effectiveness. Thus, for practical purposes, the California NPS Program is largely technology based (SWRCB and CCC, 2000).

To reduce N pollution from agricultural sources, the NPS Program focuses on implementation of MPs that promote efficient use of nutrients and irrigation water. The program specifically promotes the adoption of comprehensive nutrient management plans by dischargers whose runoff impacts coastal waters or waters listed as impaired by nutrients, as well as more uniform application of irrigation water that is consistent with crop water requirements. In addition, the program provides education and outreach that is specifically aimed at reducing nutrient runoff and leaching (SWRCB and CCC, 2000), as well as technical assistance and financial incentives for MP implementation (SWRCB and CA EPA, 2004).

Although the authority for regulating agricultural nonpoint sources of N pollution in California has been in place for decades, historically these sources have received relatively little attention from regulators. This changed in 2004 when the SWRCB adopted the current NPS implementation and enforcement policy that places greater emphasis on controlling nonpoint sources (UC DANR, 2006). Since then, efforts to promote nutrient and irrigation related MPs through the administrative tools described above have increased. However, it appears that such efforts have focused primarily on discharges to nutrient impaired surface waters, despite the existence of the SWRCB’s anti-degradation policy for groundwater. As recently as 2012, there



were no permitting requirements for agricultural nonpoint source discharges of N to groundwater (Canada et al., 2012). However the situation remains in flux. As of 2013, two SWRCB initiatives, the Long-Term Irrigated Lands Regulatory Program (ILRP) and the Central Valley Salinity Alternative for Long-Term Sustainability (CV-SALTS), both address discharges of N to groundwater.

The recent policy history and renewed regulatory focus on agricultural nonpoint sources of N pollution suggest that progress in this area has been limited. Despite persistent N pollution problems, the recent progress reports from the NPS Program primarily mention N pollution as an “upcoming [policy] priority” (CCC and SWRCB, 2012) or in the context of a recently approved Total Maximum Daily Load (SWRCB and CCC, 2009). The NPS Program has demonstrated success in reducing other types of NPS pollutants—including phosphorus, sediment and pesticides—in specific cases, which speaks to the potential effectiveness of the program’s approach (SWRCB, 2010). However, there have been no state-wide assessments of the overall effectiveness of the program or of its cost-effectiveness. Moreover, transferring these successes to N problems could be complicated by the transformability of N species and the associated cross-media pollution potential.

Lessons learned from California’s NPS Program include the following: • Proper implementation of MPs can bring about significant reductions in NPS pollution.

However, implementation and thus pollution reduction has not been widespread. • Granting broad authority for pollution control does not guarantee that particular

problems will be addressed. Regulatory resources are limited and thus specific prioritization of issues is needed to achieve progress.

• While stakeholder involvement is important, relying on voluntary cooperation of dischargers is not conducive to progress. Prior to adoption of the current implementation and enforcement policy in 2004, the program had been predicated on the voluntary cooperation of dischargers, with regulatory authority reserved for cases of persistent NPS pollution or discharger recalcitrance (SWRCB and CCC, 2000). The new policy places primary emphasis on regulatory authority while still incorporating stakeholder input to a great extent.

• Agriculture is a key element of mitigating nonpoint source N pollution in California. Given the significant N discharges by agricultural nonpoint sources and their strong spatial correlation with N impacted water resources, those sources must play a central role in efforts to mitigate N pollution.



8.2.2 California’s Agricultural Water Quality Grants Program

The Agricultural Water Quality Grants Program was established in 2002 to address agricultural nonpoint source pollution and to assist growers with complying with new requirements for conditional waivers developed pursuant to Senate Bill 390 (Chapter 686, Statutes 1999). To help growers comply with the waivers, financial assistance programs were established to work in tandem with regulatory programs to provide outreach and education, coordination, technical assistance, and financial incentives to agricultural stakeholders to identify sources of pollutants and implement measures to address discharges from irrigated agriculture. Financial assistance has been made available to growers through the Agricultural Water Quality Grants Program, the NPS Grants Program, Agricultural Drainage Loan/Agricultural Drainage Management Loan Programs, and the State Water Board’s Clean Water State Revolving Fund (CWSRF), a low interest loan program.

The initial focus of the Irrigated Lands Regulatory Program and the Agricultural Water Quality Grants Program was to reduce pollutants from agricultural operations into surface waters. Through the Agricultural Water Quality Grants Program and the CWA Section 319(h) Programs, grants are awarded to public agencies, and, in some cases, non-profit organizations or tribes through a competitive grant selection process. Grant amounts have ranged from $250,000 to $1 million with a required match ranging from 20% to 50%. Examples of eligible project types include projects that improve agricultural water quality through monitoring, demonstration projects, research, and construction of agricultural drainage improvements, as well as projects that reduce pollutants in agricultural drainage water through reuse, integrated management, or treatment. Funding has also been directed to high priority areas identified by the Regional Water Boards, and to farms along waterways where agricultural coalition water quality monitoring programs have identified problems associated with releases from irrigated agriculture. These grants pay 50% of the cost to install BMPs such as drip/micro-irrigation systems, retention ponds and recirculation systems on farms. Federal CWA Section 319(h) funding historically has been focused on agricultural projects; however, the focus in recent years has been on NPS projects in general.

Lessons learned from California’s Agricultural Water Quality Grants Program include: • Cross-jurisdictional conflicts can severely limit participation and effectiveness. The

program requires disclosure of BMP locations and monitoring points, which producers view as both intrusive and a potential liability, and which conflicts with privacy provisions of the Farm Bill. This requirement has significantly limited program participation. Furthermore, the General Obligation Bond Law requires that projects be



capital improvements with a useful life of at least 15 years; however, most BMPs have a much shorter useful life which can disqualify their eligibility for such funding.

• Timely documentation of progress is problematic. Cumulative impacts of water quality improvement projects, including compliance with water quality standards, generally take longer to realize than the time provided to implement a grant.

• Evolving state finances can hinder projects already in progress. The California “bond freeze” of 2008 impaired the ability of grantees and subcontractors to complete the work or receive payment for work completed, resulting in a number of stopped or delayed projects. Long-term successful grant programs are contingent upon a secure and stable source of funding.

• Matching fund requirements can undermine BMP implementation. Some applicants leverage funding from sources such as EQIP to fund the BMP implementation phase. However, because EQIP is a voluntary program, NRCS cannot force farmers to choose particular management practices and thus desired BMPs may not be installed. Furthermore, because EQIP has lesser reporting requirements than the Agricultural Water Quality Grants Program, the program has incomplete information on the types of management practices that are actually installed.

• Grants can facilitate outreach, education and technical assistance, as well as learning about BMP effectiveness under varying practical conditions.

8.2.3 California’s Central Coast Agricultural Waiver Program

California’s 1969 Porter-Cologne Act established the State Water Resources Control Board and gave broad authority to nine Regional Water Quality Control Boards, or “Regional Boards,” to regulate water quality at a local level. Included in the Regional Board’s jurisdiction is the right to waive the discharge permits required for any industry that releases pollutants into state waters. In an effort to encourage more robust water quality protection, the state legislature passed Senate Bill 390 (1999), which reasserted the onus on the Regional Boards to attach conditions to waivers and review them every five years. While all nine Regional Boards waive discharge requirements for all irrigated lands, each region takes a different approach to control agricultural runoff. Currently, four of the nine Regional Boards (Los Angeles, Central Coast, Central Valley and San Diego) have adopted a Conditional Agricultural Waiver.

In 2004, California’s Central Coast Region (Region 3) was the first in the state to adopt a Conditional Agricultural Waiver. The conditions attached to the 2004 waiver required growers to enroll in the Agricultural Waiver program, complete 15 hours of water quality education, prepare



a farm management plan, implement water quality improvement practices, and complete individual or cooperative water quality monitoring. When the 2004 Agricultural Waiver expired in July 2009, substantial data from the cooperative monitoring program and scientific studies demonstrated that water bodies in the region continued to be severely impaired from agricultural runoff. Because the Agricultural Waiver acts as the primary regulatory mechanism to achieve section 303(d) of the Clean Water Act for most Central Coast agricultural areas, the Regional Board was required to update the expired waiver and include provisions that would address pollutants known to cause water impairments. The Central Coast Regional Board did not have a quorum to adopt a new Agricultural Waiver in 2009, therefore the order was extended with minor modifications several times.

After nearly three years of negotiation, on March 15, 2012 the Central Coast Water Quality Control Board passed a new Conditional Agricultural Waiver (hereafter referred to as the “2012 Ag Waiver”). The updated and more comprehensive 2012 Ag Waiver places farms in one of three tiers, based on their risk to water quality (Tier 1 being the lowest risk and Tier 3 the highest), and imposes a different set of requirements for each tier. For Tier 1 and 2 farms, the requirements are similar to those in the 2004 order with two notable additions: groundwater monitoring (all tiers) and total N application reporting (for some Tier 2 and Tier 3 farms). Tier 3 farms, on the other hand, must comply with several new rigorous provisions, including individual discharge monitoring and reporting, developing and implementing an irrigation and nutrient management plan as well as nutrient balance targets. The most contentious of these additional requirements are individual surface water and groundwater monitoring. While more edge-of-field data are needed to determine contributions from individual nonpoint sources, growers are concerned about the privacy and value of individual discharge information as well as being regulated as point source dischargers. To get out of Tier 3 and avoid the more rigorous requirements, dozens of growers have partitioned their land and/or stopped using the two pesticides—diazinon and chlorpyrifos—that qualify a grower for a higher tier. Since 2012, the number of growers in Tier 3 has dropped from 111 to about 40.

Mounting scientific evidence (see Harter et al., 2012) of nitrate groundwater contamination as well as pressure from environmentalists and environmental justice groups elevated the nitrate issue to the top of the agenda during the 2012 Ag Waiver negotiation process. Consequently, a discharger’s risk to nitrate pollution is weighed heavily in the tiering criteria and conditions. For example, growers with large farms and crops that have a high potential to discharge N to groundwater are automatically placed in a higher tier with more stringent requirements. As mentioned in Chapter 8, regulating nitrates is complicated by hydrogeological



and biogeochemical processes that create time lags in water quality response. Even with additional data from Tier 3 farms, it may take decades for Agricultural Waiver controls to affect nitrate concentrations.

Time lags and other factors, such as limited nitrate substitutes, make certain policy tools previously used for other pollutants not applicable to nitrates. For example, the regulatory strategy employed in the 2012 Ag Waiver for diazinon and chlorpyrifos, both relatively dispensable pesticides with short half-lives, would not have the same effect on nitrates. Most growers decided to give up using diazinon and chlorpyrifos altogether (perhaps switching to other pesticides, which may have unintended consequences) rather than comply with Tier 3 requirements. This response would not be expected with nitrates for at least two reasons. First, reducing the use of or finding a substitute for the valuable fertilizer would be difficult, if not impossible. Second, the threat of individual monitoring requirements is greater for growers applying short half-life pesticides because they could be identified as a discharger in a short time frame. Contrast that with growers applying nitrates, who, with the same information requirements, would likely not be pinpointed as a polluter until well after their lease is up or they have retired. Lessons learned from the Central Coast include:

• Establish more comprehensive data collection and reporting. Policy makers lack quality information to adequately enforce, evaluate, and use as the baseline for modeling efforts. More individual surface water and groundwater would help determine the impacts of nutrient and chemical applications. Additionally, data are needed on environmental impacts, financial costs, and stakeholder opinions of water pollution abatement tools.

• Modest policy changes have fallen short of achieving agricultural water quality goals. The updated 2012 Ag Waiver marginally expanded what was required of the vast majority of most growers (over 97% of growers are in Tier 1 and 2). However, widespread water quality improvements have not been realized. Many remain skeptical that the new provisions will amount to little more than the previous 2004 waiver in the usefulness of information.

• Raise awareness of the water quality problem and actions will follow. Both Agricultural Waivers have successfully brought attention to the severity of water pollution in the region. As a result, farmers and farm advisory agents are rethinking nutrient management and discharges from irrigated agriculture.

• Scientific reports can have powerful implications for policy making. Several scientific studies on both nitrates (e.g., Harter et al., 2012) and pesticides (see Granite Canyon Lab,



UC Davis) played a pivotal role in prioritizing pollutants of concern in the 2012 Ag Waiver.

8.2.4 California’s Dairy Nitrogen Regulations

California’s dairy industry is one component of its agricultural enterprise and a significant source of both ammonia and nitrate emissions, as documented in this assessment. Dairies are responsible for the majority of ammonia emissions to the atmosphere and approximately one third of nitrate emissions to groundwater. While crop-only operations emit the majority of nitrates to groundwater, dairies present unique problems. Foremost among these is that N is unavoidably generated as a waste by-product of milk production, rather than imported as needed for soil amendment. The economics of milk production are such that far more waste N is produced than can be utilized by surrounding cropland, resulting in nitrate leaching rates that can be ten times higher than at crop-only operations (Pang et al., 1997; Van der Schans, 2001). California’s dairies tend to be large and thus qualify as Concentrated Animal Feeding Operations (CAFOs), which are regulated as point sources under federal law. This means dairies are subject to a different set of regulations than crop-only operations that are classified as nonpoint sources. Regardless, the physical and economic characterization of N emissions from dairies remains nonpoint, and thus these sources present the same pollution abatement challenges as crop-only operations.

The major federal environmental law currently affecting CAFOs is the Clean Water Act (CWA). Under the CWA, discharges of pollutants from point sources to waters of the United States are subject to the National Pollutant Discharge Elimination System (NPDES) permitting requirements. The CWA defines animal production facilities of certain CAFOs as point sources. The United States Environmental Protection Agency (EPA) began setting effluent limitations guidelines (ELGs) and NPDES permitting regulations for CAFOs in the mid-1970s.

Due to persistent pollution problems from animal feeding operations, the United States Department of Agriculture (USDA) and EPA released the Unified National Strategy for Animal Feeding Operations in 1999. The strategy established the goal that “all AFO owners and operators should develop and implement technically sound, economically feasible, and site specific comprehensive nutrient management plans (NMPs) to minimize impacts on water quality and public health.” (USDA and EPA, 1999). The strategy involves a comprehensive suite of both voluntary and regulatory programs. Voluntary programs (locally led conservation, environmental education, and financial/technical assistance) cover the majority of AFOs while regulatory programs (NPDES permits) focus on high risk AFOs. To achieve the goals of the



strategy, the EPA published the CAFO Final Rule in 2003. This rule can be seen as a part of the regulatory program proposed by the strategy: (1) CAFOs that actually discharge are required to apply for NPDES permits, and (2) a NMP for animal manure is required to be submitted as part of a CAFO’s NPDES permit application. The EPA authorizes a majority of states to administer the NPDES permit program within a state permit program.

In California, Title 27 of the California Code of Regulations and the Porter-Cologne Water Quality Control Act (California Water Code Division 7) governs discharges from CAFOs. The State Water Resources Control Board and nine semi-autonomous Regional Water Quality Control Boards develop guidelines under both the federal and state regulations. In 2007, the Central Valley Water Board adopted the Waste Discharge Requirements General Order for Existing Milk Cow Dairies (General Order). The General Order is essentially a local permit program in the Central Valley Region, where over 80% of California’s dairies are located (CDFA 2013). All dairies covered under the General Order are required to (1) submit a Waste Management Plan for the production area, (2) develop and implement a NMP for all land application areas, (3) monitor wastewater, soil, crops, manure, surface water discharges, and storm water discharges, (4) monitor surface water and groundwater, (5) keep records for the production and land application areas, and (6) submit annual monitoring reports. A key component of each NMP is a N budget which establishes N application rates for each crop in each land application area. The budget counts N in solid and liquid manure, irrigation water, and fertilizer. The types and frequencies of sampling, reporting, and record keeping are established by the Monitoring and Reporting Program (MRP) of the General Order. The MRP was modified in 2011 to require dairy dischargers to comply with groundwater monitoring requirements either by participating in a representative monitoring program or through individual groundwater monitoring. The Central Valley Water Board reissued the General Order in 2013 to set representative and individual groundwater monitoring programs as the primary tool to identify if manure management practices are protective of groundwater quality and include time schedules for dairy dischargers to implement improvements if monitoring data indicate that certain facilities or practices are not protective of groundwater quality.

Atmospheric pollutants from dairies are regulated under the federal Clean Air Act. Emissions of ammonia, nitrous oxide, volatile organic compounds (VOCs) and particulate matter under 10 microns (PM10) from CAFOs are primarily affected by the National Ambient Air Quality Standards (NAAQS) set by EPA under the Clean Air Act. The California Air Resources Board implements the NAAQS through a state implementation plan. Local air districts develop rules that are consistent with the requirements of California Senate Bill 700 to specify



mitigation practices for CAFOs. In 2004, the South Coast Air Quality Management District adopted the nation’s first air quality regulation (Rule 1127) to reduce ammonia, VOCs and PM10 from dairies, which includes best management practices and specific requirements regarding manure removal, handling, and composting. The San Joaquin Air Pollution Control District has regulated VOCs from dairies since 2005 but does not regulate N emissions.

The California Dairy Quality Assurance Program (CDQAP) plays an important role in helping dairies comply with these regulations. The CDQAP Environmental Stewardship Module is a voluntary partnership between dairy producers, government agencies and academia to protect the environment. It provides classroom teaching and independent third-party certification. Education courses help dairy producers understand environmental regulatory requirements, familiarize them with best management practice options, and supply record-keeping tools for both regulatory purposes and farm management. The certification program assists dairy producers in compliance with environmental regulations through a third-party, on-farm evaluation, which provides real-time feedback on management plan implementation.

Similar to California’s nonpoint program, the recent changes to the dairy regulations suggest that past policies have not achieved desired emission reductions. An exception is the effect of NPDES permitting, which is believed to have significantly reduced discharges to surface waters (Kratzer and Shelton, 1998). A key contributing factor to this success is the relative ease of observing discharges to surface water from manure handling and storage facilities, which can be accomplished through aerial photography or visual inspections, combined with strong enforcement and significant penalties for noncompliance (Doug Patteson, SWRCB Region 5; personal communication, March 12, 2015). However, N emissions to groundwater and the atmosphere are more difficult to monitor and remain persistent problems. The effects of the more recent regulatory changes remain largely unknown. Although it has been six years since the adoption of the General Order, the representative and individual groundwater monitoring programs are still under construction, so there is very limited data on nitrate levels of groundwater around dairy operations. Furthermore, hydrogeological and biogeochemical processes create time lags in water quality response, so it can take years to decades for source control programs like the General Order to affect groundwater nitrate concentrations at monitoring wells. The air quality in the South Coast Air Quality Management District has improved significantly over the past two decades, but the rate of improvement has slowed in the last several years. The effectiveness of the Rule 1127 is uncertain. The emissions from area sources (including dairies) are not monitored. Instead, they are calculated from activity information and emission factors.



The contribution of improved dairy operation management to better ambient air quality is largely unknown. Lessons learned from California’s dairy N regulations include the following:

• Classification of CAFOs as point sources, and the associated regulatory effort, has mitigated N emissions to surface waters. The remaining problems of CAFO emissions to groundwater and the atmosphere appear to be largely due to the more onerous monitoring problem and associated lack of prioritization by regulatory agencies.

• CDQAP plays an important role in helping dairies comply with regulations. This is an example of how a voluntary, largely information-based policy can be effectively used in a supporting role.

8.2.5 California’s Regulation of Atmospheric Nitrogen Emissions

Farming and livestock operations are significant sources of N emissions in California, and bear some of the negative effects of N pollution as well. Agriculture-related N air pollution results from primary emissions from machinery and vehicles employed in production, chemical compounds used in production (e.g., pesticides), as well as emissions from the agricultural systems themselves. For example, agricultural livestock emit nitrogen compounds such as oxides of N (NOx) and ammonia. Vehicles used in agricultural production emit NOx (Canadian EPA, 2004). These emissions may lead to the formation of secondary air pollutants, such as ozone, that are deleterious to workers as well as crops (Winer et al., 1990).

California is divided into 35 air districts, each with its own set of laws and regulations regarding stationary sources. Among the many different laws and regulations governing each of the 35 air districts in California, policies that regulate N air emissions include: (1) an agricultural burning policy that regulates open outdoor fires used in disposal of waste generated from growing of crops, the raising of animals, and other agribusiness operations, or for purposes such as forest management, range improvement, irrigation system management (canal clearing), (2) a policy that imposes limits on NOx emissions, and (3) a policy on the disposal of animal carcasses (“reduction of animal matter”) that requires that the gases, vapors and gas-entrained effluents from any article, machine equipment, or other contrivance used for this purpose be incinerated or processed.

Research on the effects of these local regulations on air quality has found that none of these three types of policies has had a significant effect on N air pollution, as measured by the number of exceedances of the NO2 standard (Lin, 2013, 2011).



8.2.6 North Carolina’s Neuse River Basin From the 1960s through the 1990s, the estuary of North Carolina’s 6000-square mile Neuse River Basin experienced an estimated 30% increase in N and phosphorus loadings due, in large part, to a region that experienced a doubling of its population, a five-fold increase in its number of business establishments, and a 50% increase in crop production (Schwabe, 2001, 2000). The abundance of nutrient loadings led to low dissolved oxygen levels, and extensive blue-green algal blooms during the summer months. In 1988, nutrient loadings reached such a level throughout the Neuse River as to warrant a basin-wide Nutrient Sensitive Waters classification. Then, during the summer of 1995, an unusually high level of precipitation, coupled with two major swine waste spills and an already nutrient-laden river basin resulted in conditions responsible for fish kills of over 11 million fish and huge algal blooms that rendered the Neuse River useless for recreation. In addition to the nearly anoxic conditions that caused plant and marine life to suffocate, considerable evidence has been accumulated indicating the presence of toxic dinoflagellates, organisms that can kill fish and have caused adverse respiratory health effects on humans under laboratory conditions (Burkholder, 1995).

In response to the deteriorating water quality conditions, the North Carolina Environmental Management Commission (EMC) adopted, in 1997, the state’s first mandatory plan to control both point and nonpoint source pollution in the basin (EPA, 2013a). The plan targeted a reduction in N loadings by 30%, as measured at the mouth of the estuary, by 2003. While numerous sources were targeted for mandatory reductions, including point sources, urban sources, and rural sources, agricultural sources were required to participate in The Neuse Nutrient Strategy Agricultural Rule (NCDENR, 2013). Specifically, agricultural operators were required to participate in one of two options: (1) participate in the Local Nitrogen Strategy that would include specific plans for each farm that would, collectively, meet the 30% N reduction goal, or (2) implement Standard Best Management Practices (e.g., vegetative buffer strips, water control structures, and nutrient management plans). Option 1 was unique in that it allowed agricultural agencies and farmers to work in concert to find the most cost-effective and site-specific strategy for reducing N loadings. Alternatively, for those farmers who were not interested in participating in a joint effort, they could choose among one or more alterative BMPs to achieve the 30% reduction, with obvious flexibility. The Neuse Nutrient Strategy Agricultural Rule, along with the other components of the North Carolina EMC’s point and nonpoint source management programs, was extremely successful. By the five year targeted adoption date of 2003, nutrient loadings were reduced by 42%, exceeding the 30% target. The development, implementation, and continued management of these policies required (and continues to require



and encourage) tremendous input from the agricultural community as well as extensive coordination and communication between local and state agencies and the agricultural community.

Lessons learned from the Neuse River include: • Including nonpoint sources was critical in achieving an efficient and effective nutrient

reduction outcome. Nonpoint sources produced most of the pollution and had lower abatement costs.

• Flexibility is crucial for cost-effectiveness. Farmers were allowed to achieve the 30% reduction as a coordinated group, where the group would decide how to achieve the reductions through changes in cropping patterns, implementation of BMPs and/or nutrient management plans, or through individual farmers implementing one or more strategies. Furthermore, the authority for developing management plans was effectively devolved to individual counties, thus enabling local conditions to help determine the most effective local approaches.

• Success hinged on concerted collaboration and communication among agencies, stakeholders, and the public. The partnership included the North Carolina Division of Water Quality, North Carolina Division of Soil and Water Conservation, Soil and Water Conservation Districts, North Carolina Cooperative Extension Service, North Carolina Farm Bureau, Duke University, North Carolina State University, Neuse River Foundation, USDA/NRCS, and local agricultural, environmental, and scientific communities. Together, these partners committed more than $12 million to meet project goals from 1997 through 2002.

8.2.7 The Mississippi-Atchafalaya River Basin

The Mississippi-Atchafalaya River Basin contains about 40% of the contiguous United States (including parts of 31 states). Thirty year annual and spring trends (1980–2010) of nitrate concentrations from the watershed show increases of 17% and 25% respectively (Murphy et al., 2013). Sources of nutrients include point sources and nonpoint sources, with agricultural land being the largest single contributor. Much of this comes from the highly productive, rich soils of the central corn belt. The river basin empties into the Gulf of Mexico which exhibits a seasonal hypoxic zone that is the second largest in the world. Since 1983, the annual variation in the zone size has been large, ranging from 40 km2 to over 20,000 km2 (Rabalais, n.d., accessed 28 January 2014). This variability is largely driven by weather as high water flows from the river basin deliver large



amounts of N and phosphorus, the two key nutrients leading to the creation of hypoxia in the Gulf (EPA SAB, 2007). However, a five-year running average of the zone size remains large and shows no obvious downward trajectory (Rabalais, n.d.).

While the size of the zone has been well documented, the impacts to the ecosystem are less clearly understood. Nutrient loadings can actually increase fishery production prior to the development of seasonal hypoxia, but they may also increase the yield of less valuable species at the expense of more valuable ones (Turner, 2001). And short-run beneficial effects may be outweighed by long run effects on habitat and reproductive productivity. Hypoxia has not been shown to have effects on white shrimp yields in the Gulf, but it has been found to affect brown shrimp via alteration of habitat and post-larval migration patterns (Craig, 2012; O’Connor and Whitall, 2007; Zimmerman and Nance, 2001).

The primary policy response to the growing evidence of hypoxic conditions in the Gulf was the development of a Mississippi River/Gulf of Mexico Hypoxia Task Force in 1997 (EPA, 2014). This task force consists of five federal agencies and the primary states in the Mississippi-Atchafalaya River Basin. In 2001, the task force released its “Action Plan for Reducing, Mitigating, and Controlling Hypoxia in the Northern Gulf of Mexico” where they set a target for reducing the 5-year average size of the hypoxic area to be less than 5,000 km2 by 2015. The task force called for voluntary actions (in conjunction with incentives and education) to achieve these goals. A new action plan was formulated in 2008 which preserved the goal of 5,000 km2 by 2015, though it was acknowledged that the goal was unlikely to be met. The EPA Science Advisory Board report in 2007 projected that reductions of N and phosphorus in the range of 40% - 50% would be needed to achieve this long term goal.

In addition to identifying a goal for the size of the zone, the state members of the task force committed to developing nutrient reduction strategies for their states. As of December 2013, nine of the twelve states have completed their strategies. While each differs, the focus of the state strategies remains on voluntary action, particularly from nonpoint agricultural sources. To date there has been a general lack of progress in meeting the goals developed by the action plan.

Lessons learned from the Gulf of Mexico include: • Participation in costly voluntary efforts tends to be low in the absence of private returns or

compensation. If financial incentives were provided that at least fully compensated farmers for their costs (including a small return to their effort), then reliance on voluntary measures may have been more successful. Furthermore, limited conservation budgets hinder the ability to provide such compensation.



• Establishment of nutrient reduction plans can help clarify challenges and focus research efforts. Scientists are exploring new ways to keep nutrients on the land via the development of new technologies such as bioreactors and saturated buffers. States are also beginning to fund conservation practices that are more directly related to the nutrient problem (particularly N), such as the new cover crop initiative in Iowa.

8.2.8 Maryland’s Nutrient Management Program The Chesapeake Bay Program was created in 1984 in response to concerns about nonpoint source nutrient pollution in the Chesapeake Bay. This program now includes all 5 states in the Chesapeake Bay Watershed (Virginia, Maryland, West Virginia, Delaware, Pennsylvania and New York), the District of Columbia and the US EPA. Each state sought methods to reduce nutrient loads to the Chesapeake Bay. In Maryland, the University of Maryland Cooperative Extension (UMD CE) created the Maryland Nutrient Management Program in 1988. This voluntary program teamed UMD CE personnel with growers to write and implement nutrient management plans. The initial focus of the program was on N application and use. Nutrient management plans were written to cover all bioavailable sources of N (i.e. commercial fertilizer, manure, compost, biosolids, and crop residue) during a 3-year period, including the effects of expected crop rotations and N mineralization. The plans used soil tests, manure tests, other nutrient credits (e.g., cover crops) to calculate bioavailable N, plant available phosphorus and potassium. UMD CE scientists created recommendations for nutrient application rates for approximately 20 major Maryland crops. The nutrient management plans matched the nutrient sources with the UMD CE crop recommendations to create nutrient application (management) plans.

The initial program concentrated on animal operations, though crop-only operations participated as well. The focus of these early efforts was on N applications. There existed imbalances between crop N, phosphorus and potassium demand, and manure N, phosphorus and potassium supplies. Thus, applying animal manures at N recommendations often led to over applications of phosphorus (and sometime potassium). Therefore, while the nutrient management planning program in Maryland was decreasing N use, the impact on phosphorus use remained unclear. The lack of sufficient watershed wide reductions in both N and phosphorus loads were implicated in the outbreak of Pfiesteria piscicida in the late summer of 1997 (Bosch et al., 2001). In response to this outbreak and the lack of progress on Chesapeake Bay clean-up, Maryland law makers passed the Maryland Water Quality Improvement Act (WQIA) of 1998. This act controls the use of N and phosphorus in agriculture, horticulture, turf



grass, landscape, residential and golf course settings. It also sets additional restrictions on animal producers (i.e. feed formulation) and has incentives for agriculture to change from all animal manure sources of nutrients to commercial fertilizer (Simpson, 1998).

The WQIA requires all farmers with more than $2500 in revenue or eight animal units to obtain and follow a nutrient management plan. Recognizing the then limited capacity to write nutrient management plans, this requirement was phased in over a five year period. Expanding on the original approach, these nutrient management plans incorporate the Phosphorus Site Index (PSI) number for each field. The PSI was created by the University of Maryland as a tool to estimate the potential for environmental movement of phosphorus from the fields (University of Maryland, 2013). The PSI determines whether farmers can apply nutrients at the N recommendation, the phosphorus recommendation, or a hybrid of the two.

To create the needed capacity to write nutrient management plans for all farms in Maryland, the WQIA provided funding to UMD CE to hire additional nutrient management plan writers. It also set aside funding to allow UMD CE and the Maryland Department of Agriculture (MDA) to create and implement a training and certification program for private sector crop consultants, fertilizer dealers and farmers to write nutrient management plans (farmers could not be certified to write plans for their own farms unless additional training was undertaken).

Information in the plans was considered by farmers to be confidential business information. Lawsuits in the early 2000s ruled that the plans submitted to MDA were not confidential. To protect farmer confidentiality, Maryland changed the reporting requirements. Currently, growers have to send a short summary of their nutrient management plan to MDA while retaining the full nutrient management plan on the farm. The full nutrient management plan must be made available on-farm for MDA or Maryland Department of the Environment (MDE) inspection. This arrangement allows the full nutrient management plans to remain confidential under the Freedom of Information Act.

According to the Chesapeake Bay Program’s models, implementation of the nutrient management plan requirements have and will continue to offer improvements to the Bay (Chesapeake Bay Program, 2013a). Though water quality was improving in the 1990s and 2000s, the Chesapeake Bay was not meeting water quality goals. In 2010, the US Environmental Protection Agency released a Total Maximum Daily Load (TMDL) for the entire Chesapeake Bay watershed (EPA, 2013b). Implementation of the TMDL is the responsibility of the states. The new TMDL set even lower nutrient targets than previous agreements. Thus, in 2012, Maryland modified its nutrient management requirements to include setbacks from streams for all nutrient



applications, livestock in-stream restrictions, requirements for injection or incorporation of all organic nutrient applications, and restrictions on fall and winter nutrient applications. While modeling efforts predict that these changes in nutrient management will have a significant impact on water quality in the Chesapeake Bay (Chesapeake Bay Program, 2013b), it is still too early to fully assess their effectiveness.

Lessons learned from the Chesapeake Bay include: • A narrow focus on mitigating N pollution can create other nutrient pollution problems.

Consideration of relationships between N and other nutrients used in agricultural production is needed, particularly in the presence of organic wastes.

• Issues of public disclosure of private information can be a significant obstacle. However, careful crafting of policy requirements can overcome this.

• Simulation models suggest nutrient management plans may have significant effects on water quality, but evidence to confirm efficacy in practice is pending.

8.2.9 Florida’s Everglades The Everglades Agricultural Area (EAA) consists of a portion (2,833 km2) of the original Florida Everglades and is farmed mainly to sugarcane, winter vegetables and sod. The EAA is situated north of the Everglades and south of Lake Okeechobee. The EAA basin is comprised of organic soils (Histosols) that were drained at the beginning of the century for agricultural and urban purposes. The Florida Everglades biotic integrity is endangered by urban and agricultural development, modifications to the hydrology and fire frequency, and nutrient-rich runoff from the EAA (Richardson, 2008; SFWMD, 1999). To farm successfully, growers in the EAA must actively drain their fields via an extensive array of canals, ditches and large volume pumps. Excess water is pumped off farms into South Florida Water Management District (SFWMD) canals and, historically, was sent to Lake Okeechobee or the Everglades Protection Area. Concerns about the quality of drainage water leaving the EAA basin and entering the Everglades National Park and the greater Everglades Protection Area prompted the Florida legislature to adopt the Everglades Regulatory Program, part of the Everglades Forever Act (EFA). The main objective of the program is to reduce annual phosphorus loads from the EAA basin by 25% or more compared to a 10-year, pre-BMP baseline period (1978-1988) by implementing BMPs. The EFA mandates a nonpoint regulatory source control program to implement BMPs to control phosphorus at the source and a monitoring program to assess program effectiveness. Monitoring of this NPS pollution problem was possible due to the existence of the drainage system which collects and channels NPS emissions to points where they



can be measured. The EFA further mandates the specific methodology for defining permissible total phosphorus loading levels for the basin based on historical data or baseline periods defined in the EFA (SFWMD, 2013). The program also includes the establishment of stormwater treatment areas (STAs) which are constructed wetlands for further treatment of the water before reaching the Everglades National Park. The EFA mandates an agricultural privilege tax (currently at $24.89 per acre) for the basin to be used towards the funding of Everglades restoration. Although the program does not fund BMP implementation up to the 25% reduction target, tax incentives are provided for reductions beyond the target (Kling, 2013).

The BMP program was implemented basin wide in 1995. The SFWMD requires a permit for a BMP plan for each farm basin within the EAA. The BMP plans are comprehensive, generally consisting of nutrient management, water management, and sediment control (Daroub et al., 2011). Each permit holder must select and implement a minimum of 25 “points” worth of BMPs from a suite of BMPs. Point values are assigned to BMPs based on the professional judgement of the district’s Everglades Regulation Division staff (Whalen et al., 1998). By at least one important measure, the program has been a success: the EAA basin achieved a 71% total phosphorus (TP) load reduction for water year 2012 compared with the predicted load from the pre-BMP baseline period adjusted for rainfall. The total cumulative reduction in TP loads due to BMP implementation since water year 1996 is equivalent to a long-term average annual reduction of 55% (SFWMD, 2013).

In addition, because little information was available regarding the effectiveness of BMPs when the program was started in 1995, on-farm research and demonstration was provided through a collaborative effort between the University of Florida Institute of Food and Agriculture Science (UF/IFAS), SFWMD, Florida Department of Environmental Protection, and EAA growers. The original document for BMP design and plan implementation in the EAA was developed by the UF/IFAS researchers (Bottcher et al., 1997). EFA further requires EAA landowners to sponsor a program of BMP research, testing, and implementation that monitors the efficacy of established BMPs in improving water quality in the Everglades Protection Area. To fund these and related outreach efforts, EAA growers are taxed $3 to $5 per acre. These funds support ongoing research to improve the selection, design criteria, and implementation of BMPs by the UF/IFAS. Because important and practical findings of ongoing research incorporated into agricultural practices are essential to meet and maintain the performance goals and to optimize the regulatory program, updates to documentation for individual BMPs are made available online. The UF/IFAS also conducts biannual BMP training workshops to update and refresh all EAA growers with latest technology and effectiveness of BMPs.



Lessons learned from the Florida Everglades include: • A combination of mandatory BMP participation, grower-funded research and extension

programs, and permit requirements has been very successful in reducing phosphorus runoff pollution. The unique presence of the drainage system facilitated measuring environmental improvements.

• Allowing selection of BMPs from a menu improves cost-effectiveness, though not as much as a tradable permit market. However, a complete economic analysis is not available.

8.2.10 Pennsylvania’s Conestoga River Watershed1

In the mid-1990s, the Conestoga Watershed in southeastern Pennsylvania was a Section 303(d) listed watershed due to phosphorus impairment. Agricultural sources were determined to be the primary contributor to the nutrient load. Rather than offering subsidies for voluntary BMP installation and maintenance, concerned environmental groups and their partners secured a USDA/NRCS Conservation Innovation grant to fund two reverse auctions for phosphorus abatement by producers. The auctions allowed producers to submit bids for installing and maintaining one or more BMPs on their properties. In the first auction, producers submitted bids to install BMPs at the standard EQIP subsidy rates, while in the second auction producers also submitted bid prices. In both auctions, bidders worked with Lancaster County Conservation District technicians to use computer models to estimate their expected phosphorus reductions based on site-specific characteristics. In the second auction, these estimated reductions were used with the bid prices to determine a cost-per-pound of phosphorus abatement for each bid. Bids were then ranked by cost-effectiveness from lowest to highest cost-per-pound, and contracts were awarded in order of cost-effectiveness until the auction budget was exhausted. The first auction produced an average bid price of $10.32 per pound of phosphorus, while the second auction produced an average price of $5.06. Together, the auctions mitigated an estimated 92,000 pounds of phosphorus. Using data on actual EQIP contracts in the Conestoga River Watershed, Selman et al. (2008) estimate that the reverse auction was more than seven times more cost-effective than the standard BMP subsidy approach—in other words, a reverse auction would produce more than seven times as much nutrient abatement as a standard EQIP subsidy program with the same budget. Greenhalgh et al. (2007) identify several lessons learned from the Conestoga reverse auctions, including: 1 This section is based on Greenhalgh et al. (2007) and Selman et al. (2008).



• Carefully explain the purpose of the auction and the rules to all stakeholders. The first Conestoga auction did not exhaust its budget, perhaps due to confusion and uncertainty among producers.

• Simplify the auction process to promote increased participation. • Utilize accurate and user-friendly methods for estimating load reductions and abatement

costs. 8.2.11 The European experience The challenges of developing effective policies for addressing excess N in the environment are not unique to California or the United States. As such, there may also be important lessons to be learned from European efforts to develop integrated policies that mitigate the adverse effects of N pollution on environmental quality. The recently completed European Nitrogen Assessment, which was published in 2011, provides a comprehensive summary of the European Union’s (EU) environmental policy directives that impact N management and discusses some of the successes (and failures) of these policies to achieve their intended water and air quality goals.

In the context of water quality, the EU’s 1991 Nitrates Directive establishes criteria for classifying surface and ground water bodies as polluted when NO3

- concentrations are greater than 50 mg of NO3

- per liter (EC, 2010a). In addition, the Nitrates Directive requires member states to systematically (1) monitor water quality, (2) designate vulnerable zones or water bodies, and (3) establish codes for good agricultural practice (Oenema et al., 2011). In 2000, The EU also passed the Water Framework Directive which establishes water basin districts that are tasked with monitoring and improving the quality of ground, surface and coastal water bodies (EC, 2010b); Oenema et al., 2011). These water basin districts are also responsible for designating vulnerable zones and for providing regional implementation of the Nitrates Directive as well as the 1998 Drinking Water Directive (EC, 2010c) and the 2006 Groundwater Directive (EC, 2010d). The codes for good agricultural practice that are established by each member state outline a mandatory suite of practices for farmers related to manure storage, the seasonal time periods when manure and fertilizer application is prohibited, and the maximum amount of manure and/or fertilizer N that may be legally applied (e.g., a limit of 170 kg N ha-1 yr-1 as manure).

Beginning in 2003, “cross-compliance” has become a key policy mechanism used to implement various environmental directives within the EU’s Common Agricultural Policy framework (Oenema et al., 2011). In this context, cross-compliance requires farmers to comply with relevant EU Directives in order to receive CAP payments for income support through the Single Farm Payment scheme. The Single Farm Payment also requires that farmers maintain land



in “good agricultural and environmental condition” based on a pre-specified set of regional or national environmental standards. Many of these cross-compliance standards directly address agricultural N inputs and management through the good agricultural practice codes stipulated by the 1991 Nitrates Directive.

Data presented in the European Nitrogen Assessment and a related paper by van Grinsven et al. (2012) suggests that the Nitrate Directive has contributed to measurable improvements in water quality over the past two decades (Oenema et al., 2011). For instance, about 55% of rural surface water monitoring stations in EU-15 countries (EU members prior to 2004) showed decreasing concentrations of NO3

- during the 1996–2003 period (EC, 2007). Most of the improvements were observed in the western European countries of Belgium, Denmark, Netherlands, Ireland and the United Kingdom (van Grinsven et al., 2012). However, some 31% of monitoring stations showed no change in NO3

- concentrations over the same period and another 14% showed increasing NO3

- trends (EC, 2007). By comparison, the impact of the Nitrate Directive on groundwater NO3

- in shallow wells has been relatively modest and highly variable across regional monitoring stations due largely to the time lag required for changes in surface N loading to affect ground water in deep aquifers (EC, 2007; van Grinsven et al., 2012). Consequently, the impact of these policy directives has been uneven among surface and ground water resources and highly variable across regions. The 2006 Groundwater Directive is the EU’s most recent attempt to focus policy efforts in lagging areas and equip farmers and natural resource managers with the financial resources to carry out the long term task of improving and monitoring ground water quality. .

To address the air quality impacts of N and other pollutants, the 1996 Framework Directive on Ambient Air (revised in 2008) sets regional standards for ambient concentrations of NOx, O3 and PM2.5, but not for NH3 (EC, 2010e) for the EU member states. Likewise, member states must also comply with the 2001 National Emissions Ceilings Directive for precursors to ground level O3 and acid precipitation (e.g., NOx, NH3, SO2, and VOC) (EC, 2010f). The main mechanism to achieving these air quality standards is the 1996 Integrated Pollution Prevention Control Directive (EC, 2010g), which sets emission limits for various stationary and mobile combustion sources and requires implementation of pollution control measures using “best available techniques” and technologies (Oenema et al., 2011). Under these directives, agricultural producers are subject to the policies that regulate emissions from both agricultural machinery and intensive livestock operations.

These policy frameworks have also led to measurable improvements in air quality in recent decades. Between 1990 and 2006, gaseous emissions of NOx and NH3 from all EU-15



countries declined by 33% and 12% respectively, albeit with high variability among member states (Oenema et al., 2011). In the case of NOx, the decline has been due to energy and pollution policies that require the use of improved emissions control technologies (e.g., flue gas treatment, catalytic converters), whereas for NH3 emissions, the reduction is largely a function of external economic trends which have led to a contracting the European livestock herd and an overall decreased fertilizer use (neither of which is expected to happen in California in the near future).

Given the complexities of the N cycle and the social-ecological differences among EU countries, the mixed success of recent N policy initiatives appears to highlight some policy instruments that may have applications beyond the borders of Europe. In particular, the CAP’s coupling of mandatory codes of good agricultural practice that set standards for when fertilizers and manure can be applied and caps on the total amount of N applied have parallels to the regulatory policies implemented in California and other parts of the United States. Likewise, the policies requiring cross-compliance across various environmental directives in order to receive CAP income support appears to provide a strong financial incentive to adopt improved N management practices. It is worth noting that a few regions in Europe have also experimented with taxing excess nutrients to help meet the EU Nitrate Directive requirements. For instance, in the Netherlands, the Mineral Accounting System was created to estimate excess N and phosphorus flows through agricultural systems. Excess flows were then taxed at the farm scale as an incentive to reduce nutrient loading. According to Mayzelle and Harter (2011), this approach was popular for its simplicity and had strong support from the Dutch government. Furthermore, Westhoek et al. (2004) estimate that it reduced the N surplus on Dutch dairy farms by approximately 50 kg/ha with a relatively low cost to the affected farms. However, the EU determined that the approach did not go far enough to satisfy the Nitrate Directive requirements, so it was ultimately replaced with nutrient application rate standards. 8.2.12 USEPA Review of Selected Nutrient Programs In 2009, the USEPA convened a task group comprised of state and federal surface and drinking water managers who identified and framed key nutrient issues, questions, and options on how to improve and accelerate nutrient pollution prevention and reduction at the state and national level (EPA, 2009). The task group report summarizes the scope and major sources of nutrient impacts nationally, considers tools currently under existing federal authority and that are also being used by state authorities, and presents new tools or adjustments to existing tools to improve control of nutrient pollution. Next steps to better address nutrient pollution are identified as well. Here we present some of the main conclusions of the report that are most relevant for the policy challenges facing California.



The report stresses that current tools for mitigating nutrient pollution are underused and current policies are poorly coordinated. For instance, the report recommends that greater use of numeric water quality criteria and water quality assessments would result in additional TMDLs being developed for impaired waters. Both assessments and listings of impaired waters are viewed as incomplete, and there are significant opportunities for expanding NPS reduction if the authority at the federal and state levels for development, enforcement, and transparency were improved. With respect to CAFO regulations, it is felt that significant benefits in nutrient reduction could be achieved by extending regulation to smaller operations and through the regulation of off-site transport of waste. Water quality trading is thought to be underutilized, and should be encouraged and expanded to realize its full potential. With respect to CWA Section 319 grant money, its effectiveness relies on watershed plans as the primary tool for providing assistance and monitoring and thus depends on the comprehensiveness of the plan, the management of the grant funds, and how completely the plan is implemented. The farm bill includes a variety of conservation programs that provide financial and technical help to those eligible participants, yet it is dependent on the willingness of farmers to install and maintain controls that reduce nutrients as well as the state authorities to distribute the funds.

In essence, the report suggests that the CWA tools have not been implemented to the fullest extent to reduce nutrients. While the authors acknowledge that there are individual cases in which state nonpoint source programs have been highly successful in addressing individual sources of nutrients, their broader application and effectiveness has been undercut by the absence of a common multi-state framework of mandatory point and nonpoint source accountability within and across watersheds. The authors also stress that sound science, technical analysis, collaboration, and financial incentives will fail to adequately address nutrient impacts at a state-wide and national level without a common framework of responsibility and accountability for all point and nonpoint sources, with an emphasis that nonpoint sources present state and national governments with very effective and low-cost nutrient reduction opportunities.

The report makes two strong claims related to how policy can help reduce the impacts of N loadings. First, the report stresses that while agriculture contributes significantly to the problem, it has often been overlooked from a regulatory perspective; the report notes, row crop agriculture is exempt from regulation under the CWA generally and the NPDES program specifically. Consequently, there is a significant role for agriculture in future (and better coordinated and implemented) policies to reduce N pollution. Second, the report suggests that more rigorous regulation of nonpoint sources is one of the most promising tools for addressing nutrient pollution. Other promising policies that are relevant for California’s N problem include



greater use of numeric nutrient water quality criteria in discharge permitting, and green labeling. Labeling is thought to be promising due to the growth in organic farming that has occurred since national standards were introduced in 2002, and the associated reductions in nutrient pollution that are typical of organic farms. The report also identifies market-based nutrient reduction land-use incentives and the creation of a “nutrient releases inventory” as other potential incentive-based approaches to encourage and reward effective nutrient management practices on farms. The benefits of incentive-based non-regulatory tools are that they allow interested parties a reward for implementing measures that would otherwise be unaffordable and that might lead to savings in other areas. Additional tools that could be beneficial include agricultural waste composting and more fully utilizing existing grants programs to fund BMP implementation.

The report ends with discussions of specific cases in which agricultural N runoff has been addressed by states, including the following:

• Connecticut’s Nitrogen Credit Exchange Program. A point source trading program covering all publically owned treatment works (POTWs), but potentially expandable to include nonpoint sources. Appears to be highly successful, both in terms of N load reduction and cost-effectiveness.

• Delaware’s Nutrient Management Program. Requires nutrient management plans and provides training and certification for producers who generate or apply nutrients or use BMPs. Participation appears strong but reliance on education without regulation leaves questions about its environmental impact.

• Iowa’s Livestock Water Quality Facilities Program. Provides flexible, low-interest loans to producers who volunteer to mitigate nonpoint source pollution. Highly successful in terms of participation but little information is available to evaluate its environmental impact.

• Maryland’s Policy for Nutrient Cap Management and Trading. Voluntary point-nonpoint trading program. Initiated in 2008 but lacking information on its relative success to-date.

• North Carolina’s Agricultural Cost Share Program. Provides cost-sharing funds, education, and technical assistance to producers who voluntarily install BMPs. Significant measurable impacts since its inception in 1984, but lacks information to evaluate its performance against objective criteria (e.g., environmental targets, cost-effectiveness).

• Ohio’s Agricultural Pollution Abatement Program. Provides cost-sharing for voluntary BMPs. A well-established program but with little information available to evaluate its effectiveness.



• Pennsylvania’s Nutrient Trading Program. Voluntary point-nonpoint source trading program. Little publicly available information on its performance, but Selman et al. (2009) report that only five trades occurred during the first four years of the program’s implementation. However, water quality outcomes are not necessarily dependent on the number of trades.

• Virginia’s Agricultural Stewardship Act. Relies on investigation of complaints against individual producers to identify polluting aspects of agricultural operations. Producers may be required to implement BMPs within a specified timeframe. Failure to do so invokes a fine. Despite relatively greater accountability compared to other state programs, there is again very little information to judge the environmental impact.

• Wisconsin’s Nonpoint Source Performance Standards and Prohibitions. Requires compliance with and provides cost-sharing for initial installation of BMPs. Other agricultural policies utilize cross-compliance mechanisms to achieve implementation of the same BMPs. Lacks an evaluation component, so environmental impact is largely unknown.



References

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Canadian EPA, 2004. Environment Canada [WWW Document]. URL http://www.ec.gc.ca/environment_e.html

CCC, SWRCB, 2012. Nonpoint Source Annual Accomplishment Progress Report 2011-12. Chesapeake Bay Program, 2013a. About the Program [WWW Document]. URL

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GLOSSARY OF TERMS

Abiotic Non-living. Abiotic resources comprise non-living things, for instance land, water, air and minerals.6 Adaptive capacity The ability of systems, institutions, humans, and other organisms to adjust to potential damage, to take advantage of opportunities, or to respond to consequences.6

Adsorb To take up and hold (a gas, liquid, or dissolved substance) in a thin layer of molecules on the surface of a solid substance.3 Aerobic Requiring oxygen. Algal blooms A reproductive explosion of algae in a lake, river, or ocean.4 Ammonia volatilization The conversion of ammonium nitrogen to ammonia gas by soil microorganisms. This usually occurs in soils with a high pH, that is a pH greater than 7.5, which are not common in California.6 Anaerobic The absence of oxygen.6 Anoxic The total deprivation of oxygen.3 Anthropogenic Effects which relate specifically to human activities.6 Atmospheric N deposition Nitrogen deposited on land and water surfaces. In wet deposition, nitrogen in the atmosphere is incorporated into precipitation that delivers it to the surface. In dry deposition, nitrogen is deposited directly from the atmosphere onto the surface.2

Biological nitrogen fixation (BNF) Through the process of biological N-fixation (BNF), symbiotic (mutually beneficial) and nonsymbiotic organisms can fix atmospheric N2 gas into organic N forms. A few living organisms are able to utilize molecular N2 gas from the atmosphere. The best known of these are the symbiotic Rhizobia ("legume bacteria"), nonsymbiotic free-living bacteria such as Azotobacter and Clostridium, and the cyanobacteria. Generally, in a symbiotic relationship, one organism contains chlorophyll and uses light energy to produce carbohydrates. The other organism receives some of the carbohydrates and uses them as an energy source to enzymatically fix atmospheric N2 into the ammonia (NH3) form of N and thence into amino acids and other nitrogenous compounds that are nutritionally useful to the chlorophyll-containing organism.7

Biosolids Nutrient-rich organic materials resulting from the treatment of domestic sewage in a treatment facility. When treated and processed, these residuals can be recycled and applied as fertilizer to improve and maintain productive soils and stimulate plant growth.3 Broilers Young chickens produced for meat.7 Chill hours Hours during a cold season when air temperature is below a certain value. Fruit and nut trees require certain numbers of chill hours in order to blossom in the following season.2

Climate forcing (or radiative forcing) A measure of the influence that a factor (e.g. greenhouse gases, atmospheric aerosol) has in changing the balance of energy in the atmospheric system. Expressed in watts per square meter (W m-2) of the Earth's surface.2


GLOSSARY OF TERMS 1

CMAQ model A computational tool used for air quality management to produce daily forecasts for ozone air quality and to assess implementation actions needed to attain National Ambient Air Quality Standards.3 Community water system A public water system that serves at least 15 service connections used by yearlong residents or that regularly serves at least 25 yearlong residents. These systems are regulated under the Safe Drinking Water Act.1 Denitrification The reduction of nitrate into N2 via a series of enzymatic reactions by microorganisms in anaerobic environments. Denitrification may also occur under aerobic conditions with co-respiration of oxygen and nitrate. Both denitrification pathways involve formation of intermediate gases (NO and N2O) that may be lost to the atmosphere.2

Domestic well A privately owned well that supplies ground water for human consumption and other household uses.8 Ecosystem services Benefits that people derive from ecosystems.2

Effluent Water that flows from a sewage treatment plant after it has been treated.8

Emission factor The average emission rate of a given greenhouse gas (GHG) for a given source, relative to units of activity.6

Eutrophication The process by which water becomes enriched with plant nutrients, most commonly phosphorus and nitrogen.8

Growing degree days A heat index that relates the development of plants, insects, and disease organisms to environmental air temperature.5

Haber-Bosch process Method of synthesizing reactive nitrogen from hydrogen and non-reactive atmospheric N2, developed

by German physical chemist Fritz Haber in 1908 and brought to an industrial scale by Carl Bosch in 1910.2

Influent Water, wastewater, or other liquid flowing into a reservoir, basin, or treatment plant.3

Irrigation well Unregulated wells used for irrigation and other agricultural purposes, but not for drinking water.2

Leachate Water containing soluble ions and compounds collected during movement through soil. See also Leaching.2

Leaching The movement of soluble ions and compounds beyond the depth at which crop or plant roots can reach, due to rain or irrigation.2

Local small water system A water system that serves 2 to 4 households. These often draw on a single domestic well. These systems are not regulated under the Safe Drinking Water Act.1

Maximum contaminant level (MCL) Enforceable limits for nitrate and nitrite established to protect the public against consumption of drinking water that has concentrations of these contaminants high enough to present a risk to human health (e.g. 10 mg nitrate-N L-1 and 1 mg nitrite-N L-1).3

N loading Total amount of nitrogen entering an ecosystem at a given time, through both surface and subsurface transport from the surrounding landscape.2

N-fixing species Microorganisms with the biological catalyst nitrogenase, which allows the conversion of atmospheric N2 to ammonia. These microorganisms work symbiotically with certain plant species. See also biological nitrogen fixation.2

Nitrification Process by which ammonium is oxidized to nitrite and then nitrate by microorganisms under aerobic conditions.2


GLOSSARY OF TERMS 2

Nitrogen Fixation The biological or chemical process by which nitrogen from the atmosphere (N2) is converted into ammonia (NH3).2

Nitrogen use efficiency The proportion of all N inputs that are removed in harvested crop biomass, contained in recycled crop residues, and incorporated into soil organic matter and inorganic N pools.6

Non-point source A source from which pollution is discharged in a diffuse manner.2

On-site wastewater treatment systems A system relying on natural processes and/or mechanical components to collect, treat, and disperse or reclaim wastewater from a single dwelling or building.3

Organic nitrogen A nitrogen compound that had its origin in living material and is still part of a carbon-chain complex. It can enter soil as decomposed plant or animal tissue. It is not available to plants until microorganisms transform it to ammonium (NH4

+).6 Partial nutrient balance The amount of nutrient (nitrogen) in the material exported off the field divided by the amount of nutrient applied.2

Public water system A system for the provision of water for human consumption through pipes or other constructed conveyances that has 15 or more service connections or regularly serves at least 25 individuals daily at least 60 days out of the year. These systems are regulated under the Safe Drinking Water Act.1

Reactive nitrogen Any chemical form of nitrogen except dinitrogen (N2).2

Recharge Process by which rain water (precipitation) seeps into the ground-water system.3

Self-supplied water system A water system that is not connected to a public water system, is assumed to be 1 to 2 households/ dwelling units (or connections). These systems are not regulated under the Safe Drinking Water Act.1

Silage A feed prepared by chopping green forage (e.g. grass, legumes, field corn) and placing the material in a structure or container designed to exclude air. The material then undergoes fermentation, retarding spoilage. Silage has a water content of between 60 and 80%.3

Specialty crop Fruits, vegetables, tree nuts, dried fruits, and horticulture and nursery crops (including floriculture).6

State small water system A system for the provision of piped water to the public for human consumption that serves at least five, but no more than 14, service connections and does not regularly serve drinking water to more than an average of 25 individuals daily for more than 60 days out of the year. These systems are not regulated under the Safe Drinking Water Act.1

Synthetic fertilizer A commercially prepared inorganic compound of plant nutrients.2

Urea N A form of nitrogen that converts readily to ammonium.3

Volatile organic compounds (VOCs) Any organic compound that participates in atmospheric photochemical reactions except those designated by EPA as having negligible photochemical reactivity.3


GLOSSARY OF TERMS 0

References The following sources were used to derive glossary terms. 1 State Water Resources Control Board. “Information for Public Drinking Water Systems.” Accessed 29 July 2016. http://www.swrcb.ca.gov/drinking_water/certlic/drinkingwater/publicwatersystems.shtml 2 State of Washington Department of Ecology. “Nitrogen from Atmospheric Deposition.” Accessed 29 July 2016. http://www.ecy.wa.gov/programs/eap/nitrogen/NitrogenAtmosphere.html 3 US EPA Terminology Services. “Terms & Acronyms.” Accessed 29 July 2016. https://iaspub.epa.gov/sor_internet/registry/termreg/searchandretrieve/termsandacronyms/search.do 4 IPCC. 1997. “Regional Impacts of Climate Change: An Assessment of Vulnerability.” Accessed 29 July 2016. https://ipcc.ch/ipccreports/sres/regional/ 5 American Meteorology Society. “Glossary of Meteorology.” Accessed 29 July 2016. http://glossary.ametsoc.org/wiki/Growing_degree-day 6 UC Division of Agriculture and Natural Resources Solution Center for Nutrient Management. “Glossary of Nutrient Management Terms.” Accessed 29 July 2016. http://ucanr.edu/sites/Nutrient_Management_Solutions/Research_Database/Glossary_/ 7 Hatfield, J.L. and R.F. Follett. 2008. Nitrogen in the Environment. Elsevier. 8 US Geological Survey. “Volatile Organic Compounds in the Nation’s Ground Water and Drinking-Water Supply Wells: Supporting Information.” Accessed 29 July 2016. http://water.usgs.gov/nawqa/vocs/national_assessment/report/glossary.html

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